Science.gov

Sample records for atypical imaging features

  1. Acute aortic dissection: typical and atypical imaging features.

    PubMed

    Fisher, E R; Stern, E J; Godwin, J D; Otto, C M; Johnson, J A

    1994-11-01

    Acute aortic dissection (AAD) is the most common emergency affecting the aorta. Noninvasive imaging allows prompt and reliable diagnosis of AAD and has largely supplanted aortography. However, atypical imaging features and diagnostic pitfalls can delay lifesaving therapy. An intimal flap is the characteristic feature of AAD. If there is flow within both lumina, typical imaging features are probably present. If the false lumen is thrombosed or there is no intimal tear to permit flow through the false lumen, a distinct intimal flap may not be present. Secondary signs of AAD include an intramural or periaortic acute thrombus, which manifests as a high-attenuation cuff or crescent on unenhanced computed tomographic scans. Other conditions that can reduce the conspicuity of the intimal flap include atypical configurations of the flap, such as seen with short dissections or with multiple false channels, in which case the flaps are complex. Finally, aortic anomalies may cause confusion.

  2. Atypical Imaging Features of Tuberculous Spondylitis: Case Report with Literature Review

    PubMed Central

    Momjian, Rita; George, Mina

    2014-01-01

    Spinal tuberculosis in its typical form that shows destruction of two adjacent vertebral bodies and opposing end plates, destruction of the intervening intervertebral disc and a paravertebral or psoas abscess, is easily recognized and readily treated. Atypical tuberculous spondylitis without the above mentioned imaging features, although seen infrequently, has been well documented. We present, in this report, a case of atypical tuberculous spondylitis showing involvement of contiguous lower dorsal vertebral bodies and posterior elements with paravertebral and epidural abscess but with preserved intervertebral discs. The patient presented in advanced stage with progressive severe neurological symptoms due to spinal cord compression. Non-enhanced magnetic resonance imaging led to misdiagnosis of the lesion as a neoplastic process. It was followed by contrast enhanced computed tomography of the chest and abdomen that raised the possibility of an infectious process and, post-operatively, histopathological examination of the operative specimen confirmed tuberculosis. This case indicates the difficulty in differentiating atypical spinal tuberculosis from other diseases causing spinal cord compression. The different forms of atypical tuberculous spondylitis reported in the literature are reviewed. The role of the radiologist in tuberculous spondylitis is not only to recognize the imaging characteristics of the disease by best imaging modality, which is contrast enhanced magnetic resonance imaging, but also to be alert to the more atypical presentations to ensure early diagnosis and prompt treatment to prevent complications. However, when neither clinical examination nor magnetic resonance imaging findings are reliable in differentiating spinal infection from one another and from neoplasm, adequate biopsy, either imaging guided or surgical biopsy is essential for early diagnosis. PMID:25926906

  3. Progressive paraplegia caused by recurrence of mantle-cell lymphoma with atypical spinal magnetic resonance imaging features.

    PubMed

    Yamane, Hiromichi; Ochi, Nobuaki; Yamagishi, Tomoko; Takigawa, Nagio; Maeda, Yoshinobu

    2015-01-01

    We describe a case of paraplegia, which had progressed rapidly in a 60-year-old Japanese man with mantle-cell lymphoma. (MCL). He admitted to our hospital due to lumbago and progressive muscle weakness of bilateral lower thighs lasting for 1. month, while he had the history of the systemic chemotherapy for MCL since 10 months. Magnetic resonance imaging. (MRI) revealed a wide-spreading intradural tumor situated in the spinal canal from L1 to L5 with an intervertebral slipped disk as the only site of recurrence. Laminectomy followed by salvage chemotherapy led disappearance of lumbago and paraplegia of the bilateral lower extremities. Although wide-spreading tumor formation in spinal canal without other involvement sites is very rare in MCL, physicians should be aware of such patterns of central nervous system. (CNS) relapse for the early diagnosis and adequate selection of treatment modality. PMID:26881642

  4. Transformation of a meningioma with atypical imaging

    PubMed Central

    Kumar, Ashish; Deopujari, Chandrashekhar; Karmarkar, Vikram

    2016-01-01

    Meningiomas are benign tumors of the central nervous system. They have long term curability if they are excised completely. If not, they can recur after a prolonged period and can lead to increased morbidity during re-surgery. Recurrence is rarely associated with invasiveness. Usually de-differentiation in case of meningiomas is uncommon without any predisposing factors including different genetic mutations or radiation to the involved region. We report a case of a 38-year-old female who was operated for a benign para-sagittal meningioma 8 years back and subsequently developed an invasive recurrence off late. Also this time, the imaging morphology was slightly different for a meningioma and gross as well as microscopic findings were very atypical. Awareness for such cases must be there while dealing with recurrent meningiomas as invasiveness may not always be associated with adverse predisposing factors like radiation. As invasiveness is always a histopathological diagnosis, picking up such features on imaging is a daunting task and if done, can help neurosurgeons prognosticate such invasive recurrences in a better fashion. PMID:27366271

  5. Imaging pathological tau in atypical parkinsonian disorders

    PubMed Central

    Coakeley, Sarah; Strafella, Antonio P.

    2016-01-01

    Purpose of review This review examines the current literature on tau imaging in atypical parkinsonian disorders and other tauopathies. Recent findings There are a number of tau PET radiotracers that have demonstrated promising preliminary results in atypical parkinsonian disorders, such as progressive supranuclear palsy and corticobasal degeneration. These radiotracers were capable of selectively labeling tau in vitro and in vivo, with high affinity. Other radiotracers tested more extensively in patients with Alzheimer’s disease have also been able to successfully image tau deposition. Summary The development of tau radioligands for PET has led to the current testing of these tracers in clinical studies, many of which concentrate on patients with Alzheimer’s disease. Atypical parkinsonian disorders such as progressive supranuclear palsy and corticobasal degeneration are now being investigated as well. These disorders can be very difficult to diagnose, because of their clinical overlap with other parkinsonian disorders. Imaging tau using PET could serve as a diagnostic biomarker for these tauopathies and provide a means of assessing treatment that targets tau burden. PMID:26110795

  6. Atypical Intracranial Epidermoid Cysts: Rare Anomalies with Unique Radiological Features

    PubMed Central

    Law, Eric K. C.; Lee, Ryan K. L.; Ng, Alex W. H.; Siu, Deyond Y. W.; Ng, Ho-Keung

    2015-01-01

    Epidermoid cysts are benign slow growing extra-axial tumours that insinuate between brain structures, while their occurrences in intra-axial or intradiploic locations are exceptionally rare. We present the clinical, imaging, and pathological findings in two patients with atypical epidermoid cysts. CT and MRI findings for the first case revealed an intraparenchymal epidermoid cyst that demonstrated no restricted diffusion. The second case demonstrated an aggressive epidermoid cyst that invaded into the intradiploic spaces, transverse sinus, and the calvarium. The timing of ectodermal tissue sequestration during fetal development may account for the occurrence of atypical epidermoid cysts. PMID:25667778

  7. Calcifying odontogenic cyst with atypical features.

    PubMed

    Balaji, S M; Rooban, Thavarajah

    2012-01-01

    The calcifying odontogenic cyst (COC) was first delineated in 1962. It is a rare developmental odontogenic cyst with notable presence of histopathological features which include a cystic lining demonstrating characteristic "Ghost" epithelial cells with a propensity to calcify. In addition, the COC may be associated with other recognized odontogenic tumors. This gives rise to a spectrum of variants of COC according to clinical, histopathological, and radiological characteristics. Very few reports have actually captured the actual transformation while most reported cases are documents of co-existing lesions. This article presents one such entity, where the asymptomatic presentation misled the diagnosis and on histopathological examination revealed the COC with areas suggestive of adenomatoid odontogenic tumor.

  8. Atypical pyogenic brain abscess evaluation by diffusion-weighted imaging: diagnosis with multimodality MR imaging.

    PubMed

    Ozbayrak, Mustafa; Ulus, Ozden Sila; Berkman, Mehmet Zafer; Kocagoz, Sesin; Karaarslan, Ercan

    2015-10-01

    Whether a brain abscess is apparent by imaging depends on the stage of the abscess at the time of imaging, as well as the etiology of the infection. Because conventional magnetic resonance imaging (MRI) is limited in its ability to distinguish brain abscesses from necrotic tumors, advanced techniques are required. The management of these two disease entities differs and can potentially affect the clinical outcome. We report a case having atypical imaging features of a pyogenic brain abscess on advanced MRI, in particular, on diffusion-weighted and perfusion imaging, in a patient with osteosarcoma undergoing chemotherapy.

  9. Magnetic resonance imaging of liver lesions: exceptions and atypical lesions.

    PubMed

    van den Bos, Indra C; Hussain, Shahid M; de Man, Robert A; Zondervan, Pieter E; Ijzermans, Jan N M; Preda, A; Krestin, Gabriel P

    2008-01-01

    On state-of-the-art magnetic resonance imaging, most lesions can be detected and characterized with confidence according to well-known criteria. However, atypical characteristics in some common lesions and the incidental encounter with rare lesions may pose diagnostic difficulties. In this article, six challenging hepatic lesions will be discussed and evaluated on the most important magnetic resonance imaging sequences, with histological correlation when available. In addition, the background information concerning these lesions will be described based on the most recent available literature. By reading this article, the reader will be able to (1) categorize the lesion in solid and fluid-containing lesions, based on the T2 signal intensity; and (2) define the benign or malignant nature of the lesion, in relation to the signal intensity and dynamic enhancement pattern, despite the presence of atypical characteristics of some lesions. PMID:18436109

  10. Hepatic haemangioma: common and uncommon imaging features.

    PubMed

    Klotz, T; Montoriol, P-F; Da Ines, D; Petitcolin, V; Joubert-Zakeyh, J; Garcier, J-M

    2013-09-01

    The haemangioma, the most common non-cystic hepatic lesion, most often discovered by chance, may in certain situations raise diagnostic problems in imaging. In this article, the authors first demonstrate that the radiological appearance of the hepatic haemangioma, in its typical form, is closely related to three known histological sub-types. They then show that certain atypical features should be known in order to establish a diagnosis. They also observe the potential interactions between the haemangioma, an active vascular lesion, and the adjacent hepatic parenchyma. Finally, they discuss the specific paediatric features of hepatic haemangiomas and illustrate the case of a hepatic angiosarcoma.

  11. Acute Kidney Injury and Atypical Features during Pediatric Poststreptococcal Glomerulonephritis

    PubMed Central

    Ayoob, Rose M.

    2016-01-01

    The most common acute glomerulonephritis in children is poststreptococcal glomerulonephritis (PSGN) usually occurring between 3 and 12 years old. Hypertension and gross hematuria are common presenting symptoms. Most PSGN patients do not experience complications, but rapidly progressive glomerulonephritis and hypertensive encephalopathy have been reported. This paper reports 17 patients seen in 1 year for PSGN including 4 with atypical PSGN, at a pediatric tertiary care center. Seventeen children (11 males), mean age of 8 years, were analyzed. Ninety-four percent had elevated serum BUN levels and decreased GFR. Four of the hospitalized patients had complex presentations that included AKI along with positive ANA or ANCAs. Three patients required renal replacement therapy and two were thrombocytopenic. PSGN usually does not occur as a severe nephritis. Over the 12-month study period, 17 cases associated with low serum albumin in 53%, acute kidney injury in 94%, and thrombocytopenia in 18% were treated. The presentation of PSGN may be severe and in a small subset have associations similar to SLE nephritis findings including AKI, positive ANA, and hematological anomalies. PMID:27642522

  12. Acute Kidney Injury and Atypical Features during Pediatric Poststreptococcal Glomerulonephritis.

    PubMed

    Ayoob, Rose M; Schwaderer, Andrew L

    2016-01-01

    The most common acute glomerulonephritis in children is poststreptococcal glomerulonephritis (PSGN) usually occurring between 3 and 12 years old. Hypertension and gross hematuria are common presenting symptoms. Most PSGN patients do not experience complications, but rapidly progressive glomerulonephritis and hypertensive encephalopathy have been reported. This paper reports 17 patients seen in 1 year for PSGN including 4 with atypical PSGN, at a pediatric tertiary care center. Seventeen children (11 males), mean age of 8 years, were analyzed. Ninety-four percent had elevated serum BUN levels and decreased GFR. Four of the hospitalized patients had complex presentations that included AKI along with positive ANA or ANCAs. Three patients required renal replacement therapy and two were thrombocytopenic. PSGN usually does not occur as a severe nephritis. Over the 12-month study period, 17 cases associated with low serum albumin in 53%, acute kidney injury in 94%, and thrombocytopenia in 18% were treated. The presentation of PSGN may be severe and in a small subset have associations similar to SLE nephritis findings including AKI, positive ANA, and hematological anomalies. PMID:27642522

  13. Acute Kidney Injury and Atypical Features during Pediatric Poststreptococcal Glomerulonephritis

    PubMed Central

    Ayoob, Rose M.

    2016-01-01

    The most common acute glomerulonephritis in children is poststreptococcal glomerulonephritis (PSGN) usually occurring between 3 and 12 years old. Hypertension and gross hematuria are common presenting symptoms. Most PSGN patients do not experience complications, but rapidly progressive glomerulonephritis and hypertensive encephalopathy have been reported. This paper reports 17 patients seen in 1 year for PSGN including 4 with atypical PSGN, at a pediatric tertiary care center. Seventeen children (11 males), mean age of 8 years, were analyzed. Ninety-four percent had elevated serum BUN levels and decreased GFR. Four of the hospitalized patients had complex presentations that included AKI along with positive ANA or ANCAs. Three patients required renal replacement therapy and two were thrombocytopenic. PSGN usually does not occur as a severe nephritis. Over the 12-month study period, 17 cases associated with low serum albumin in 53%, acute kidney injury in 94%, and thrombocytopenia in 18% were treated. The presentation of PSGN may be severe and in a small subset have associations similar to SLE nephritis findings including AKI, positive ANA, and hematological anomalies.

  14. Seizures as an Atypical Feature of Beal's Syndrome.

    PubMed

    Jaman, Nazreen B K; Al-Sayegh, Abeer

    2016-08-01

    Congenital contractural arachnodactyly, commonly known as Beal's syndrome, is an extremely rare genetic disorder caused by mutations in the fibrillin-2 (FBN2) gene located on chromosome 5q23. It is an autosomal dominant inherited connective tissue disorder characterised by a Marfan-like body habitus, contractures, abnormally shaped ears and kyphoscoliosis. We report a seven-year-old Omani male who presented to the Sultan Qaboos University Hospital, Muscat, Oman, in 2014 with seizures. He was noted to have certain distinctive facial features and musculoskeletal manifestations; he was subsequently diagnosed with Beal's syndrome. Sequencing of the FBN2 gene revealed that the patient had a novel mutation which was also present in his mother; however, she had only a few facial features indicative of Beal's syndrome and no systemic involvement apart from a history of childhood seizures. To the best of the authors' knowledge, this is the first report of Beal's syndrome with seizure symptoms as a potential feature. PMID:27606123

  15. Toxic leukoencephalopathy with atypical MRI features following a lacquer thinner fire.

    PubMed

    Kao, Hung-Wen; Pare, Laura; Kim, Ronald; Hasso, Anton N

    2014-05-01

    Toxic leukoencephalopathy is a structural alteration of the white matter following exposure to various toxic agents. We report a 49-year-old man exposed to an explosion of lacquer thinner with brain MRI features atypical from those of chronic toxic solvent intoxication. PMID:24291481

  16. Seizures as an Atypical Feature of Beal’s Syndrome

    PubMed Central

    Jaman, Nazreen B. K.; Al-Sayegh, Abeer

    2016-01-01

    Congenital contractural arachnodactyly, commonly known as Beal’s syndrome, is an extremely rare genetic disorder caused by mutations in the fibrillin-2 (FBN2) gene located on chromosome 5q23. It is an autosomal dominant inherited connective tissue disorder characterised by a Marfan-like body habitus, contractures, abnormally shaped ears and kyphoscoliosis. We report a seven-year-old Omani male who presented to the Sultan Qaboos University Hospital, Muscat, Oman, in 2014 with seizures. He was noted to have certain distinctive facial features and musculoskeletal manifestations; he was subsequently diagnosed with Beal’s syndrome. Sequencing of the FBN2 gene revealed that the patient had a novel mutation which was also present in his mother; however, she had only a few facial features indicative of Beal’s syndrome and no systemic involvement apart from a history of childhood seizures. To the best of the authors’ knowledge, this is the first report of Beal’s syndrome with seizure symptoms as a potential feature. PMID:27606123

  17. Seizures as an Atypical Feature of Beal’s Syndrome

    PubMed Central

    Jaman, Nazreen B. K.; Al-Sayegh, Abeer

    2016-01-01

    Congenital contractural arachnodactyly, commonly known as Beal’s syndrome, is an extremely rare genetic disorder caused by mutations in the fibrillin-2 (FBN2) gene located on chromosome 5q23. It is an autosomal dominant inherited connective tissue disorder characterised by a Marfan-like body habitus, contractures, abnormally shaped ears and kyphoscoliosis. We report a seven-year-old Omani male who presented to the Sultan Qaboos University Hospital, Muscat, Oman, in 2014 with seizures. He was noted to have certain distinctive facial features and musculoskeletal manifestations; he was subsequently diagnosed with Beal’s syndrome. Sequencing of the FBN2 gene revealed that the patient had a novel mutation which was also present in his mother; however, she had only a few facial features indicative of Beal’s syndrome and no systemic involvement apart from a history of childhood seizures. To the best of the authors’ knowledge, this is the first report of Beal’s syndrome with seizure symptoms as a potential feature.

  18. Imaging in Rare and Atypical Sinonasal Masses: An Interesting Case Series

    PubMed Central

    Prasad, Akhila; Baruah, Deb Kumar; Garga, Umesh Chandra

    2015-01-01

    Sinonasal tumours present a myriad of radiographic findings. While many of these tumours have been well described with regard to their typical sites of origin, age group and radiological appearance we have come across lesions in our daily practice which are exceedingly rare with regard to site of origin in sinonasal cavity. The radiological appearances of 4 such rare and unusual tumours arising in sinonasal region evaluated by cross sectional imaging (CT/MRI) have been illustrated in this article with a purpose to review the radio-pathological correlation of these tumours and to explain the utility of cross-sectional imaging CT and MRI in exploring diagnostic clues. Morphological features and radiological patterns of each tumour have been graded into mild, moderate and severe based on the extent of tumoural involvement. This review is intended to acquaint radiologists with the appearance of atypical sinonasal masses and their radiological appearance on cross sectional imaging to make an early diagnosis. PMID:26816972

  19. Atypical features in salivary gland mixed tumors: their relationship to malignant transformation.

    PubMed

    Auclair, P L; Ellis, G L

    1996-06-01

    Although criteria for distinction between the benign and malignant elements in carcinoma ex mixed tumor have been adequately described, there have not been any attempts to identify clinical or histologic features in benign mixed tumors that indicate increased risk of malignant change. For this reason, 65 mixed tumors of the major and minor salivary glands that exhibited atypical histologic features were examined in an attempt to analyze which, if any, of these features might indicate a greater likelihood of malignant transformation. The atypical features evaluated were hypercellularity, capsule violation, hyalinization, necrosis, and cellular anaplasia. The mitotic rate was also analyzed. The age of the patient, and the site, size, and prediagnostic duration of the tumor were recorded and, together with the histologic findings, were correlated with follow-up information. Nine (13.8%) of the 65 tumors underwent malignant transformation. Five of these patients died of the tumor, two others had distant metastases and were alive with the disease, and two were free of disease. Benign mixed tumors that showed prominent zones of hyalinization or at least moderate mitotic activity were more likely to develop carcinoma than those that did not. chi 2 analysis indicated that only hyalinization was significant (P < 0.05), but, with Fisher's exact test (two-tailed), this and all of the other features evaluated revealed a P value greater than 0.05. The other atypical features failed to correlate with malignant change. Clinical findings at the initial diagnosis that indicated a greater likelihood of malignant transformation were occurrence in the submandibular gland, older patient age, and large tumor size.

  20. Atypical histopathologic features in a melanocytic nevus after cryotherapy and pregnancy.

    PubMed

    Wilford, Casey E; Brantley, Julie S; Diwan, A Hafeez

    2014-10-01

    Melanocytic nevi can undergo clinical and histopathologic changes during pregnancy, as well as after various forms of surgical and nonsurgical trauma. We report the case of a 9-month postpartum 29-year-old female who presented to her dermatologist with a clinically worrisome nevus. This nevus had been treated with liquid nitrogen by her primary care physician 6 months prior to presentation. Histopathologic evaluation revealed a crowded proliferation of atypical melanocytes at the dermal-epidermal junction overlying a scar. The dermal component contained scattered mitotic figures. A combined MART-1, tyrosinase and Ki-67 immunohistochemical study showed foci of increased melanocytic proliferation. These atypical features were interpreted as associated with both the prior cryotherapy, as well as her recent pregnancy. Knowledge of the clinical context in evaluating difficult melanocytic lesions is essential.

  1. Atypical features in pleomorphic adenoma--a clinicopathologic study and implications for management.

    PubMed

    Ethunandan, M; Witton, R; Hoffman, G; Spedding, A; Brennan, P A

    2006-07-01

    Pleomorphic adenoma is the most common salivary gland neoplasm and infrequently undergoes malignant transformation. Carcinoma ex pleomorphic adenoma is typically an infiltrative neoplasm with features of cellular pleomorphism, high mitotic activity, peri-neural and vascular invasion. More recently, sub-groups of pleomorphic adenoma have been described exhibiting vascular invasion and features of malignancy without evidence of extra-capsular extension. There is little information in the literature regarding how these different histological variants influence clinical presentation and outcome following primary treatment. Following a review of 100 consecutive pleomorphic adenomas removed from the major salivary glands, 4 cases with atypical histological features were found. Three tumours exhibited features of dysplasia/carcinoma without evidence of extra-capsular invasion and a further case demonstrated benign vascular invasion. There were no clinical features suggestive of the atypical nature of these neoplasms, though fine needle aspiration cytology (FNAC) was suspicious of a malignancy in 2 cases and CT scan in 1 case. Patients underwent a superficial parotidectomy or submandibular gland excision based on the location of the lesion. All lesions were completely excised and there were no recurrences in this series.

  2. Tuberculate supernumerary teeth: report of a case showing typical and atypical features and the management.

    PubMed

    Utomi, I L

    2012-01-01

    Tuberculate supernumerary teeth are found in the maxillary anterior region. They usually result in oral problems such as malocclusion, food impaction, poor aesthetics and cyst formation. There is paucity of literature on this anomaly in our environment. This paper describes a case of tuberculate supernumerary teeth with typical and atypical features of supernumerary teeth in this region. Treatment is carried out with a combination of surgical and orthodontic methods. Early diagnosis and treatment of this anomaly is suggested to avoid more serious consequences and to prevent severe orthodontic complications. PMID:23175913

  3. ZFP36-FOSB Fusion Defines a Subset of Epithelioid Hemangioma with Atypical Features

    PubMed Central

    Antonescu, Cristina R; Chen, Hsiao-Wei; Zhang, Lei; Sung, Yun-Shao; Panicek, David; Agaram, Narasimhan P; Dickson, Brendan C; Krausz, Thomas; Fletcher, Christopher D

    2014-01-01

    Epithelioid hemangioma (EH) is a benign neoplasm with distinctive vasoformative features, which occasionally shows increased cellularity, cytologic atypia, and/or loco-regional aggressive growth, resulting in challenging differential diagnosis from malignant vascular neoplasms. Based on two intra-osseous EH index cases with worrisome histologic features, such as the presence of necrosis, RNA sequencing was applied for possible fusion gene discovery and potential subclassification of a novel atypical EH subset. A ZFP36-FOSB fusion was detected in one case, while a WWTR1-FOSB chimeric transcript in the other, both were further validated by FISH and RT-PCR. These abnormalities were then screened by FISH in 44 EH from different locations with 7 additional EH revealing FOSB gene rearrangements, all except one being fused to ZFP36. Interestingly, 4/6 penile EH studied showed FOSB abnormalities. Although certain atypical histologic features were observed in the FOSB-rearranged EH, including solid growth, increased cellularity, mild to moderate nuclear pleomorphism, and necrosis in 3/9 cases, no overt sarcomatous areas were discerned to objectively separate the lesions from the fusion-negative EH. No patient has developed recurrence to date, but the follow-up was relatively limited and short to draw definitive conclusions regarding behavior. Although FOSB-rearranged EH do not show significant morphologic overlap with SERPINE1-FOSB fusion-positive pseudomyogenic hemangioendothelioma, FOSB oncogenic activation is emerging as an important event in these benign and intermediate groups of vascular tumors. PMID:25043949

  4. [Diagnosis and treatment of heparin-induced thrombocytopenia (HIT) based on its atypical immunological features].

    PubMed

    Miyata, Shigeki; Maeda, Takuma

    2016-03-01

    Heparin-induced thrombocytopenia (HIT) is a prothrombotic side effect of heparin therapy caused by HIT antibodies, i.e., anti-platelet factor 4 (PF4)/heparin IgG with platelet-activating properties. For serological diagnosis, antigen immunoassays are commonly used worldwide. However, such assays do not indicate their platelet-activating properties, leading to low specificity for the HIT diagnosis. Therefore, over-diagnosis is currently the most serious problem associated with HIT. The detection of platelet-activating antibodies using a washed platelet activation assay is crucial for appropriate HIT diagnosis. Recent advances in our understanding of the pathogenesis of HIT include it having several clinical features atypical for an immune-mediated disease. Heparin-naïve patients can develop IgG antibodies as early as day 4, as in a secondary immune response. Evidence for an anamnestic response on heparin re-exposure is lacking. In addition, HIT antibodies are relatively short-lived, unlike those in a secondary immune response. These lines of evidence suggest that the mechanisms underlying HIT antibody formation may be compatible with a non-T cell-dependent immune reaction. These atypical clinical and serological features should be carefully considered while endeavoring to accurately diagnose HIT, which leads to appropriate therapies such as immediate administration of an alternative anticoagulant to prevent thromboembolic events and re-administration of heparin during surgery involving cardiopulmonary bypass when HIT antibodies are no longer detectable.

  5. The typical and atypical MR imaging findings of central neurocytomas: Report on eighteen cases and review of the literature.

    PubMed

    Ma, Zhanlong; Yan, Hailang; Shi, Haibin; Li, Yan; Song, Jiacheng; Huang, Junwen; Hong, Xiongning

    2016-07-01

    There were few studies have documented the MRI features of typical and atypical CNCs for diagnosis and therapeutic modalities. Here, 18 histopathologically confirmed cases of intracranial CNCs (8 men and 10 women with a mean age of 28.3 years, range 10-64 years) were retrospectively analyzed. The histopathological and immunohistochemical features were also assessed. On MR imaging, the 14 typical cases of CNCs showed relatively round, lobulated tumor masses in the body of the right lateral ventricle (5 cases), left lateral ventricle (4 cases), third ventricles (2 cases), and midline (3 cases). These typical CNCs masses contained clusters of cysts of varying sizes and "soap bubble" appearance on T2WI; they showed mild to moderate heterogeneously enhancement on T1WI. The 4 atypical cases of CNCs showed as strongly contrast enhancement of the tumors with the attachment or infiltrate of the wall of the ventricle than the typical benign cases. These atypical CNCs were in the right lateral ventricle (2 cases), left lateral ventricle (1 case), and third ventricle (1 case). Microscopically, the typical CNCs were well-differentiated tumors with benign histological features. The typical and atypical CNCs were composed of uniform, small to medium-sized cells with rounded nuclei and scant cytoplasm. Immunohistochemically, the typical CNCs were strong in Syn immunopositive (14/14) and neuron-specific enolase (12/14). The atypical CNC tumor cells showed malignant behavior and more positive expression of Ki67 than the benign cases. Surgery is the first choice of treatment, and radiotherapy may be beneficial to postoperative patients. PMID:27132079

  6. IgG4-related lung disease with atypical CT imaging: a case report

    PubMed Central

    Zhou, Jiaxuan; Li, Xian

    2014-01-01

    IgG4-related lung disease is a rare disease, diagnosed when typical pathologic features are seen in the context of increased serum levels of IgG4 and the elevated tissue’s IgG4-positive plasma cells. Here we reported the case of a 24-year-old woman with IgG4-related lung disease. This patient presented with fever, cough and shortness of breath. Thoracic computed tomography (CT) images demonstrated multiple nodules or masses with high density in both lungs, and thickened interlobular septa. The ‘halo sign’ was observed around the high-density lesions of the upper lobes. This range of CT images’ characteristics is atypical, which differs from previous reports of this condition. PMID:25590008

  7. Image segmentation using random features

    NASA Astrophysics Data System (ADS)

    Bull, Geoff; Gao, Junbin; Antolovich, Michael

    2014-01-01

    This paper presents a novel algorithm for selecting random features via compressed sensing to improve the performance of Normalized Cuts in image segmentation. Normalized Cuts is a clustering algorithm that has been widely applied to segmenting images, using features such as brightness, intervening contours and Gabor filter responses. Some drawbacks of Normalized Cuts are that computation times and memory usage can be excessive, and the obtained segmentations are often poor. This paper addresses the need to improve the processing time of Normalized Cuts while improving the segmentations. A significant proportion of the time in calculating Normalized Cuts is spent computing an affinity matrix. A new algorithm has been developed that selects random features using compressed sensing techniques to reduce the computation needed for the affinity matrix. The new algorithm, when compared to the standard implementation of Normalized Cuts for segmenting images from the BSDS500, produces better segmentations in significantly less time.

  8. Atypical imaging of spinal tuberculosis: a case report and review of literature

    PubMed Central

    Zhang, Huijun; Lu, Zenghui

    2016-01-01

    This is a case report of spinal tuberculosis combined with sacroiliac joint tuberculosis, pulmonary tuberculosis, chest wall tuberculosis and tuberculous pleurisy and the image of the patient is rare, special and not typical and it looks like a halo sign. It has an important reference value for the diagnosis of spine tuberculosis although it is a rare imaging manifestation and diagnosis was confirmed by pathology after the surgery. Therefore atypical imaging is often appeared in clinical practice and it is meaningful and necessary for the diagnosis of atypical spinal tuberculosis combined with multiple organ tuberculosis. PMID:27642440

  9. Atypical imaging of spinal tuberculosis: a case report and review of literature.

    PubMed

    Zhang, Huijun; Lu, Zenghui

    2016-01-01

    This is a case report of spinal tuberculosis combined with sacroiliac joint tuberculosis, pulmonary tuberculosis, chest wall tuberculosis and tuberculous pleurisy and the image of the patient is rare, special and not typical and it looks like a halo sign. It has an important reference value for the diagnosis of spine tuberculosis although it is a rare imaging manifestation and diagnosis was confirmed by pathology after the surgery. Therefore atypical imaging is often appeared in clinical practice and it is meaningful and necessary for the diagnosis of atypical spinal tuberculosis combined with multiple organ tuberculosis. PMID:27642440

  10. Atypical features of a Ure2p glutathione transferase from Phanerochaete chrysosporium.

    PubMed

    Thuillier, Anne; Roret, Thomas; Favier, Frédérique; Gelhaye, Eric; Jacquot, Jean-Pierre; Didierjean, Claude; Morel-Rouhier, Mélanie

    2013-07-11

    Glutathione transferases (GSTs) are known to transfer glutathione onto small hydrophobic molecules in detoxification reactions. The GST Ure2pB1 from Phanerochaete chrysosporium exhibits atypical features, i.e. the presence of two glutathione binding sites and a high affinity towards oxidized glutathione. Moreover, PcUre2pB1 is able to efficiently deglutathionylate GS-phenacylacetophenone. Catalysis is not mediated by the cysteines of the protein but rather by the one of glutathione and an asparagine residue plays a key role in glutathione stabilization. Interestingly PcUre2pB1 interacts in vitro with a GST of the omega class. These properties are discussed in the physiological context of wood degrading fungi.

  11. Detection of Variable Gaseous Absorption Features in the Debris Disks Around Young A-type Stars

    NASA Astrophysics Data System (ADS)

    Montgomery, Sharon L.; Welsh, Barry Y.

    2012-10-01

    We present medium resolution (R = 60,000) absorption measurements of the interstellar Ca II K line observed towards five nearby A-type stars (49 Ceti, 5 Vul, ι Cyg, 2 And, and HD 223884) suspected of possessing circumstellar gas debris disks. The stars were observed on a nightly basis during a six night observing run on the 2.1-meter Otto Struve telescope at the McDonald Observatory, Texas. We have detected nightly changes in the absorption strength of the Ca II K line observed near the stellar radial velocity in three of the stars (49 Ceti, i Cyg and HD 223884). Such changes in absorption suggest the presence of a circumstellar (atomic) gas disk around these stars. In addition to the absorption changes in the main Ca II K line profile, we have also observed weak transient absorption features that randomly appear at redshifted velocities in the spectra of 49 Ceti, 5 Vul, and 2 And. These absorption features are most probably associated with the presence of falling evaporated bodies (exo-comets) that liberate evaporating gas on their approach to the central star. This now brings the total number of systems in which exocomet activity has been observed at Ca II or Na I wavelengths on a nightly basis to seven (β Pic, HR 10, HD 85905, β Car, 49 Ceti, 5 Vul, and 2 And), with 2 And exhibiting weaker and less frequent changes. All of the disk systems presently known to exhibit either type of short-term variability in Ca II K line absorption are rapidly rotating A-type stars (V sin i > 120 km s-1). Most exhibit mid-IR excesses, and many of them are very young (< 20 Myr), thus supporting the argument that many of them are transitional objects between Herbig Ae and "Vega-like" A-type stars with more tenuous circumstellar disks. No mid-IR excess (due to the presence of a dust disk) has yet been detected around either 2 And or HD 223884, both of which have been classified as λ Boötis-type stars. This may indicate that the observed changes in gas absorption for these two

  12. Characterization of the Autophagy Marker Protein Atg8 Reveals Atypical Features of Autophagy in Plasmodium falciparum

    PubMed Central

    Allanki, Aparna Devi; Sijwali, Puran Singh

    2014-01-01

    Conventional autophagy is a lysosome-dependent degradation process that has crucial homeostatic and regulatory functions in eukaryotic organisms. As malaria parasites must dispose a number of self and host cellular contents, we investigated if autophagy in malaria parasites is similar to the conventional autophagy. Genome wide analysis revealed a partial autophagy repertoire in Plasmodium, as homologs for only 15 of the 33 yeast autophagy proteins could be identified, including the autophagy marker Atg8. To gain insights into autophagy in malaria parasites, we investigated Plasmodium falciparum Atg8 (PfAtg8) employing techniques and conditions that are routinely used to study autophagy. Atg8 was similarly expressed and showed punctate localization throughout the parasite in both asexual and sexual stages; it was exclusively found in the pellet fraction as an integral membrane protein, which is in contrast to the yeast or mammalian Atg8 that is distributed among cytosolic and membrane fractions, and suggests for a constitutive autophagy. Starvation, the best known autophagy inducer, decreased PfAtg8 level by almost 3-fold compared to the normally growing parasites. Neither the Atg8-associated puncta nor the Atg8 expression level was significantly altered by treatment of parasites with routinely used autophagy inhibitors (cysteine (E64) and aspartic (pepstatin) protease inhibitors, the kinase inhibitor 3-methyladenine, and the lysosomotropic agent chloroquine), indicating an atypical feature of autophagy. Furthermore, prolonged inhibition of the major food vacuole protease activity by E64 and pepstatin did not cause accumulation of the Atg8-associated puncta in the food vacuole, suggesting that autophagy is primarily not meant for degradative function in malaria parasites. Atg8 showed partial colocalization with the apicoplast; doxycycline treatment, which disrupts apicoplast, did not affect Atg8 localization, suggesting a role, but not exclusive, in apicoplast

  13. Image feature localization by multiple hypothesis testing of Gabor features.

    PubMed

    Ilonen, Jarmo; Kamarainen, Joni-Kristian; Paalanen, Pekka; Hamouz, Miroslav; Kittler, Josef; Kälviäinen, Heikki

    2008-03-01

    Several novel and particularly successful object and object category detection and recognition methods based on image features, local descriptions of object appearance, have recently been proposed. The methods are based on a localization of image features and a spatial constellation search over the localized features. The accuracy and reliability of the methods depend on the success of both tasks: image feature localization and spatial constellation model search. In this paper, we present an improved algorithm for image feature localization. The method is based on complex-valued multi resolution Gabor features and their ranking using multiple hypothesis testing. The algorithm provides very accurate local image features over arbitrary scale and rotation. We discuss in detail issues such as selection of filter parameters, confidence measure, and the magnitude versus complex representation, and show on a large test sample how these influence the performance. The versatility and accuracy of the method is demonstrated on two profoundly different challenging problems (faces and license plates).

  14. Systemic mastocytosis presenting as intraoperative anaphylaxis with atypical features: a report of two cases.

    PubMed

    Bridgman, D E; Clarke, R; Sadleir, P H M; Stedmon, J J; Platt, P

    2013-01-01

    Two cases of perioperative cardiovascular collapse are presented that were associated with markedly elevated mast cell tryptase levels shortly after the event, leading to the assumption that an immunoglobin E-mediated, drug-induced anaphylaxis had occurred. However, the clinical picture in both cases was atypical and subsequent skin testing failed to identify a triggering drug. Further blood tests, some weeks later, revealed persistently elevated baseline levels of mast cell tryptase. In both cases bone marrow biopsy and genetic testing confirmed the diagnosis of mastocytosis. We present evidence and speculate that mast cell degranulation was triggered by tourniquet release in the first case and by exposure to peanuts in the second. An atypical presentation of anaphylaxis should alert the anaesthetist to the possibility of previously undiagnosed mastocytosis. PMID:23362901

  15. Image fusion using sparse overcomplete feature dictionaries

    DOEpatents

    Brumby, Steven P.; Bettencourt, Luis; Kenyon, Garrett T.; Chartrand, Rick; Wohlberg, Brendt

    2015-10-06

    Approaches for deciding what individuals in a population of visual system "neurons" are looking for using sparse overcomplete feature dictionaries are provided. A sparse overcomplete feature dictionary may be learned for an image dataset and a local sparse representation of the image dataset may be built using the learned feature dictionary. A local maximum pooling operation may be applied on the local sparse representation to produce a translation-tolerant representation of the image dataset. An object may then be classified and/or clustered within the translation-tolerant representation of the image dataset using a supervised classification algorithm and/or an unsupervised clustering algorithm.

  16. Feature encoding for color image segmentation

    NASA Astrophysics Data System (ADS)

    Li, Ning; Li, Youfu

    2001-09-01

    An approach for color image segmentation is proposed based on the contributions of color features to segmentation rather than the choice of a particular color space. It is different from the pervious methods where SOFM is used for construct the feature encoding so that the feature-encoding can self-organize the effective features for different color images. Fuzzy clustering is applied for the final segmentation when the well-suited color features and the initial parameter are available. The proposed method has been applied in segmenting different types of color images and the experimental results show that it outperforms the classical clustering method. Our study shows that the feature encoding approach offers great promise in automating and optimizing color image segmentation.

  17. Image segmentation using association rule features.

    PubMed

    Rushing, John A; Ranganath, Heggere; Hinke, Thomas H; Graves, Sara J

    2002-01-01

    A new type of texture feature based on association rules is described. Association rules have been used in applications such as market basket analysis to capture relationships present among items in large data sets. It is shown that association rules can be adapted to capture frequently occurring local structures in images. The frequency of occurrence of these structures can be used to characterize texture. Methods for segmentation of textured images based on association rule features are described. Simulation results using images consisting of man made and natural textures show that association rule features perform well compared to other widely used texture features. Association rule features are used to detect cumulus cloud fields in GOES satellite images and are found to achieve higher accuracy than other statistical texture features for this problem.

  18. Featured Image: A Double Cluster

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2015-11-01

    This is a color composite image from Hubble of the very young star cluster Westerlund 2, seen near the center of the image (click for the full view!). The image was produced using visible-light data from the Advanced Camera for Surveys and near-infrared data from the Wide Field Camera 3. A recently-published study, led by Peter Zeidler (Center for Astronomy at Heidelberg University), reports the results of a high-resolution multi-band survey of the Westerlund 2 region with Hubble. In their detailed analysis of the cluster, the authors cataloged over 17,000 objects in six different filters! They find that the cluster actually consists of two separate clumps that were born at the same time but have different stellar densities. For more information and the original image, see the paper here:CitationPeter Zeidler et al 2015 AJ 150 78. doi:10.1088/0004-6256/150/3/78

  19. Featured Image: Solar Prominence Eruptions

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2016-02-01

    In these images from the Solar Dynamics Observatorys AIA instrument (click for the full resolution!), two solar prominence eruptions (one from June 2011 and one from August 2012) are shown in pre- and post-eruption states. The images at the top are taken in the Fe XII 193 bandpass and the images at the bottom are taken in the He II 304 bandpass. When a team of scientists searched through seven years of solar images taken by the STEREO (Solar Terrestrial Relations Observatory) spacecraft, these two eruptions were found to extend all the way out to a distance of 1 AU. They were the only two examples of clear, bright, and compact prominence eruptions found to do so. The scientists, led by Brian Wood (Naval Research Laboratory), used these observations to reconstruct the motion of the eruption and model how prominences expand as they travel away from the Sun. Theimage to the rightshowsa STEREO observation compared to the teams 3D model of theprominences shape and expansion. To learn more about theresults from this study, check out the paper below.CitationBrian E. Wood et al 2016 ApJ 816 67. doi:10.3847/0004-637X/816/2/67

  20. Atypical Cities

    ERIC Educational Resources Information Center

    DiJulio, Betsy

    2011-01-01

    In this creative challenge, Surrealism and one-point perspective combine to produce images that not only go "beyond the real" but also beyond the ubiquitous "imaginary city" assignment often used to teach one-point perspective. Perhaps the difference is that in the "atypical cities challenge," an understanding of one-point perspective is a means…

  1. Quantitative imaging features: extension of the oncology medical image database

    NASA Astrophysics Data System (ADS)

    Patel, M. N.; Looney, P. T.; Young, K. C.; Halling-Brown, M. D.

    2015-03-01

    Radiological imaging is fundamental within the healthcare industry and has become routinely adopted for diagnosis, disease monitoring and treatment planning. With the advent of digital imaging modalities and the rapid growth in both diagnostic and therapeutic imaging, the ability to be able to harness this large influx of data is of paramount importance. The Oncology Medical Image Database (OMI-DB) was created to provide a centralized, fully annotated dataset for research. The database contains both processed and unprocessed images, associated data, and annotations and where applicable expert determined ground truths describing features of interest. Medical imaging provides the ability to detect and localize many changes that are important to determine whether a disease is present or a therapy is effective by depicting alterations in anatomic, physiologic, biochemical or molecular processes. Quantitative imaging features are sensitive, specific, accurate and reproducible imaging measures of these changes. Here, we describe an extension to the OMI-DB whereby a range of imaging features and descriptors are pre-calculated using a high throughput approach. The ability to calculate multiple imaging features and data from the acquired images would be valuable and facilitate further research applications investigating detection, prognosis, and classification. The resultant data store contains more than 10 million quantitative features as well as features derived from CAD predictions. Theses data can be used to build predictive models to aid image classification, treatment response assessment as well as to identify prognostic imaging biomarkers.

  2. Featured Image: Modeling Supernova Remnants

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2016-05-01

    This image shows a computer simulation of the hydrodynamics within a supernova remnant. The mixing between the outer layers (where color represents the log of density) is caused by turbulence from the Rayleigh-Taylor instability, an effect that arises when the expanding core gas of the supernova is accelerated into denser shell gas. The past standard for supernova-evolution simulations was to perform them in one dimension and then, in post-processing, manually smooth out regions that undergo Rayleigh-Taylor turbulence (an intrinsically multidimensional effect). But in a recent study, Paul Duffell (University of California, Berkeley) has explored how a 1D model could be used to reproduce the multidimensional dynamics that occur in turbulence from this instability. For more information, check out the paper below!CitationPaul C. Duffell 2016 ApJ 821 76. doi:10.3847/0004-637X/821/2/76

  3. Detecting Nematode Features from Digital Images

    PubMed Central

    de la Blanca, N. Pérez; Fdez-Valdivia, J.; Castillo, P.; Gómez-Barcina, A.

    1992-01-01

    Procedures for estimating and calibrating nematode features from digitial images are described and evaluated by illustration and mathematical formulae. Technical problems, such as capturing and cleaning raw images, standardizing the grey level range of images, and the detection of characteristics of the body habitus, presence or absence of stylet knobs, and tail and lip region shape are discussed. This study is the first of a series aimed at developing a set of automated methods to permit more rapid, objective characterizations of nematode features than is achievable by cumbersome conventional methods. PMID:19282998

  4. Solitary fibrous tumor with atypical histological features occurring in the palatine tonsil: an uncommon neoplasm in an uncommon site.

    PubMed

    Macarenco, Ricardo Silvestre; Bacchi, Carlos E; Domingues, Maria Aparecida Custódio

    2006-11-01

    Solitary fibrous tumor (SFT) is an uncommon mesenchymal neoplasm that usually arises in the pleura. Although this tumor has been described in other sites, including the head and neck area, in the oropharynx it is extremely rare. We report the first case of a SFT arising from the palatine tonsil of a 62-year-old man. The tumor consisted of spindle-shaped cells distributed in a haphazard pattern and presented atypical histological features such as hypercellular areas and high mitotic count. Immunohistochemical studies showed strong positivity for CD34 and bcl-2, and weak positivity for desmin. Smooth muscle actin, S-100 protein and cytokeratines were negative. The patient was well without disease 1 year after surgery.

  5. Automatic extraction of planetary image features

    NASA Technical Reports Server (NTRS)

    LeMoigne-Stewart, Jacqueline J. (Inventor); Troglio, Giulia (Inventor); Benediktsson, Jon A. (Inventor); Serpico, Sebastiano B. (Inventor); Moser, Gabriele (Inventor)

    2013-01-01

    A method for the extraction of Lunar data and/or planetary features is provided. The feature extraction method can include one or more image processing techniques, including, but not limited to, a watershed segmentation and/or the generalized Hough Transform. According to some embodiments, the feature extraction method can include extracting features, such as, small rocks. According to some embodiments, small rocks can be extracted by applying a watershed segmentation algorithm to the Canny gradient. According to some embodiments, applying a watershed segmentation algorithm to the Canny gradient can allow regions that appear as close contours in the gradient to be segmented.

  6. Hemorrhage detection in MRI brain images using images features

    NASA Astrophysics Data System (ADS)

    Moraru, Luminita; Moldovanu, Simona; Bibicu, Dorin; Stratulat (Visan), Mirela

    2013-11-01

    The abnormalities appear frequently on Magnetic Resonance Images (MRI) of brain in elderly patients presenting either stroke or cognitive impairment. Detection of brain hemorrhage lesions in MRI is an important but very time-consuming task. This research aims to develop a method to extract brain tissue features from T2-weighted MR images of the brain using a selection of the most valuable texture features in order to discriminate between normal and affected areas of the brain. Due to textural similarity between normal and affected areas in brain MR images these operation are very challenging. A trauma may cause microstructural changes, which are not necessarily perceptible by visual inspection, but they could be detected by using a texture analysis. The proposed analysis is developed in five steps: i) in the pre-processing step: the de-noising operation is performed using the Daubechies wavelets; ii) the original images were transformed in image features using the first order descriptors; iii) the regions of interest (ROIs) were cropped from images feature following up the axial symmetry properties with respect to the mid - sagittal plan; iv) the variation in the measurement of features was quantified using the two descriptors of the co-occurrence matrix, namely energy and homogeneity; v) finally, the meaningful of the image features is analyzed by using the t-test method. P-value has been applied to the pair of features in order to measure they efficacy.

  7. Automatic Feature Extraction from Planetary Images

    NASA Technical Reports Server (NTRS)

    Troglio, Giulia; Le Moigne, Jacqueline; Benediktsson, Jon A.; Moser, Gabriele; Serpico, Sebastiano B.

    2010-01-01

    With the launch of several planetary missions in the last decade, a large amount of planetary images has already been acquired and much more will be available for analysis in the coming years. The image data need to be analyzed, preferably by automatic processing techniques because of the huge amount of data. Although many automatic feature extraction methods have been proposed and utilized for Earth remote sensing images, these methods are not always applicable to planetary data that often present low contrast and uneven illumination characteristics. Different methods have already been presented for crater extraction from planetary images, but the detection of other types of planetary features has not been addressed yet. Here, we propose a new unsupervised method for the extraction of different features from the surface of the analyzed planet, based on the combination of several image processing techniques, including a watershed segmentation and the generalized Hough Transform. The method has many applications, among which image registration and can be applied to arbitrary planetary images.

  8. Wood Recognition Using Image Texture Features

    PubMed Central

    Wang, Hang-jun; Zhang, Guang-qun; Qi, Heng-nian

    2013-01-01

    Inspired by theories of higher local order autocorrelation (HLAC), this paper presents a simple, novel, yet very powerful approach for wood recognition. The method is suitable for wood database applications, which are of great importance in wood related industries and administrations. At the feature extraction stage, a set of features is extracted from Mask Matching Image (MMI). The MMI features preserve the mask matching information gathered from the HLAC methods. The texture information in the image can then be accurately extracted from the statistical and geometrical features. In particular, richer information and enhanced discriminative power is achieved through the length histogram, a new histogram that embodies the width and height histograms. The performance of the proposed approach is compared to the state-of-the-art HLAC approaches using the wood stereogram dataset ZAFU WS 24. By conducting extensive experiments on ZAFU WS 24, we show that our approach significantly improves the classification accuracy. PMID:24146821

  9. Onboard Image Registration from Invariant Features

    NASA Technical Reports Server (NTRS)

    Wang, Yi; Ng, Justin; Garay, Michael J.; Burl, Michael C

    2008-01-01

    This paper describes a feature-based image registration technique that is potentially well-suited for onboard deployment. The overall goal is to provide a fast, robust method for dynamically combining observations from multiple platforms into sensors webs that respond quickly to short-lived events and provide rich observations of objects that evolve in space and time. The approach, which has enjoyed considerable success in mainstream computer vision applications, uses invariant SIFT descriptors extracted at image interest points together with the RANSAC algorithm to robustly estimate transformation parameters that relate one image to another. Experimental results for two satellite image registration tasks are presented: (1) automatic registration of images from the MODIS instrument on Terra to the MODIS instrument on Aqua and (2) automatic stabilization of a multi-day sequence of GOES-West images collected during the October 2007 Southern California wildfires.

  10. Feature detection algorithms in computed images

    NASA Astrophysics Data System (ADS)

    Gurbuz, Ali Cafer

    2008-10-01

    The problem of sensing a medium by several sensors and retrieving interesting features is a very general one. The basic framework is generally the same for applications from MRI, tomography, Radar SAR imaging to subsurface imaging, even though the data acquisition processes, sensing geometries and sensed properties are different. In this thesis we introduced a new perspective to the problem of remote sensing and information retrieval by studying the problem of subsurface imaging using GPR and seismic sensors. We have shown that if the sensed medium is sparse in some domain then it can be imaged using many fewer measurements than required by the standard methods. This leads to much lower data acquisition times and better images. We have used the ideas from Compressive Sensing, which show that a small number of random measurements about a signal are sufficient to completely characterize it, if the signal is sparse or compressible in some domain. Although we have applied our ideas to the subsurface imaging problem, our results are general and can be extended to other remote sensing applications. A second objective in remote sensing is information retrieval which involves searching for important features in the computed image. In this thesis we focus on detecting buried structures like pipes, and tunnels in computed GPR or seismic images. The problem of finding these structures in high clutter and noise conditions, and finding them faster than the standard shape detecting methods is analyzed. One of the most important contributions of this thesis is where the sensing and the information retrieval stages are unified in a single framework using compressive sensing. Instead of taking lots of standard measurements to compute the image of the medium and search the necessary information in the computed image, only a small number of measurements as random projections are used to infer the information without generating the image of the medium.

  11. Imaging features of benign adrenal cysts.

    PubMed

    Sanal, Hatice Tuba; Kocaoglu, Murat; Yildirim, Duzgun; Bulakbasi, Nail; Guvenc, Inanc; Tayfun, Cem; Ucoz, Taner

    2006-12-01

    Benign adrenal gland cysts (BACs) are rare lesions with a variable histological spectrum and may mimic not only each other but also malignant ones. We aimed to review imaging features of BACs which can be helpful in distinguishing each entity and determining the subsequent appropriate management. PMID:16962278

  12. Infantile Cellular Schwannoma Developing on the Skin with Atypical Clinical Features

    PubMed Central

    Fujimura, Taku; Tagami, Hachiro; Aiba, Setsuya

    2014-01-01

    Cellular schwannoma (CS) is a variety of schwannoma with a predominantly cellular growth, normally developing in middle-aged patients. In this report, we describe a 15-month-old infant with primary cutaneous CS on the knee. Because of its histologically malignant features, CS is sometimes overdiagnosed as a malignant nerve tumor. Therefore, awareness of this variant of schwannoma is important for dermatologists to avoid needless treatments for patients with CS. PMID:25232315

  13. Multimodal imaging of temporal processing in typical and atypical language development.

    PubMed

    Kovelman, Ioulia; Wagley, Neelima; Hay, Jessica S F; Ugolini, Margaret; Bowyer, Susan M; Lajiness-O'Neill, Renee; Brennan, Jonathan

    2015-03-01

    New approaches to understanding language and reading acquisition propose that the human brain's ability to synchronize its neural firing rate to syllable-length linguistic units may be important to children's ability to acquire human language. Yet, little evidence from brain imaging studies has been available to support this proposal. Here, we summarize three recent brain imaging (functional near-infrared spectroscopy (fNIRS), functional magnetic resonance imaging (fMRI), and magnetoencephalography (MEG)) studies from our laboratories with young English-speaking children (aged 6-12 years). In the first study (fNIRS), we used an auditory beat perception task to show that, in children, the left superior temporal gyrus (STG) responds preferentially to rhythmic beats at 1.5 Hz. In the second study (fMRI), we found correlations between children's amplitude rise-time sensitivity, phonological awareness, and brain activation in the left STG. In the third study (MEG), typically developing children outperformed children with autism spectrum disorder in extracting words from rhythmically rich foreign speech and displayed different brain activation during the learning phase. The overall findings suggest that the efficiency with which left temporal regions process slow temporal (rhythmic) information may be important for gains in language and reading proficiency. These findings carry implications for better understanding of the brain's mechanisms that support language and reading acquisition during both typical and atypical development.

  14. Multimodal imaging of temporal processing in typical and atypical language development.

    PubMed

    Kovelman, Ioulia; Wagley, Neelima; Hay, Jessica S F; Ugolini, Margaret; Bowyer, Susan M; Lajiness-O'Neill, Renee; Brennan, Jonathan

    2015-03-01

    New approaches to understanding language and reading acquisition propose that the human brain's ability to synchronize its neural firing rate to syllable-length linguistic units may be important to children's ability to acquire human language. Yet, little evidence from brain imaging studies has been available to support this proposal. Here, we summarize three recent brain imaging (functional near-infrared spectroscopy (fNIRS), functional magnetic resonance imaging (fMRI), and magnetoencephalography (MEG)) studies from our laboratories with young English-speaking children (aged 6-12 years). In the first study (fNIRS), we used an auditory beat perception task to show that, in children, the left superior temporal gyrus (STG) responds preferentially to rhythmic beats at 1.5 Hz. In the second study (fMRI), we found correlations between children's amplitude rise-time sensitivity, phonological awareness, and brain activation in the left STG. In the third study (MEG), typically developing children outperformed children with autism spectrum disorder in extracting words from rhythmically rich foreign speech and displayed different brain activation during the learning phase. The overall findings suggest that the efficiency with which left temporal regions process slow temporal (rhythmic) information may be important for gains in language and reading proficiency. These findings carry implications for better understanding of the brain's mechanisms that support language and reading acquisition during both typical and atypical development. PMID:25773611

  15. Atypical Features in a Large Turkish Family Affected with Friedreich Ataxia

    PubMed Central

    Cevik, Betul; Aksoy, Durdane; Sahbaz, E. Irmak; Basak, A. Nazli

    2016-01-01

    Here, we describe the clinical features of several members of the same family diagnosed with Friedreich ataxia (FRDA) and cerebral lesions, demyelinating neuropathy, and late-age onset without a significant cardiac involvement and presenting with similar symptoms, although genetic testing was negative for the GAA repeat expansion in one patient of the family. The GAA repeat expansion in the frataxin gene was shown in all of the family members except in a young female patient. MRI revealed arachnoid cysts in two patients; MRI was consistent with both cavum septum pellucidum-cavum vergae and nodular signal intensity increase in one patient. EMG showed demyelinating sensorimotor polyneuropathy in another patient. The GAA expansion-negative 11-year-old female patient had mental-motor retardation, epilepsy, and ataxia. None of the patients had significant cardiac symptoms. Description of FRDA families with different ethnic backgrounds may assist in identifying possible phenotypic and genetic features of the disease. Furthermore, the genetic heterogeneity observed in this family draws attention to the difficulty of genetic counseling in an inbred population and to the need for genotyping all affected members before delivering comprehensive genetic counseling.

  16. Atypical Features in a Large Turkish Family Affected with Friedreich Ataxia.

    PubMed

    Kurt, Semiha; Cevik, Betul; Aksoy, Durdane; Sahbaz, E Irmak; Gundogdu Eken, Aslı; Basak, A Nazli

    2016-01-01

    Here, we describe the clinical features of several members of the same family diagnosed with Friedreich ataxia (FRDA) and cerebral lesions, demyelinating neuropathy, and late-age onset without a significant cardiac involvement and presenting with similar symptoms, although genetic testing was negative for the GAA repeat expansion in one patient of the family. The GAA repeat expansion in the frataxin gene was shown in all of the family members except in a young female patient. MRI revealed arachnoid cysts in two patients; MRI was consistent with both cavum septum pellucidum-cavum vergae and nodular signal intensity increase in one patient. EMG showed demyelinating sensorimotor polyneuropathy in another patient. The GAA expansion-negative 11-year-old female patient had mental-motor retardation, epilepsy, and ataxia. None of the patients had significant cardiac symptoms. Description of FRDA families with different ethnic backgrounds may assist in identifying possible phenotypic and genetic features of the disease. Furthermore, the genetic heterogeneity observed in this family draws attention to the difficulty of genetic counseling in an inbred population and to the need for genotyping all affected members before delivering comprehensive genetic counseling. PMID:27668106

  17. Atypical Features in a Large Turkish Family Affected with Friedreich Ataxia

    PubMed Central

    Cevik, Betul; Aksoy, Durdane; Sahbaz, E. Irmak; Basak, A. Nazli

    2016-01-01

    Here, we describe the clinical features of several members of the same family diagnosed with Friedreich ataxia (FRDA) and cerebral lesions, demyelinating neuropathy, and late-age onset without a significant cardiac involvement and presenting with similar symptoms, although genetic testing was negative for the GAA repeat expansion in one patient of the family. The GAA repeat expansion in the frataxin gene was shown in all of the family members except in a young female patient. MRI revealed arachnoid cysts in two patients; MRI was consistent with both cavum septum pellucidum-cavum vergae and nodular signal intensity increase in one patient. EMG showed demyelinating sensorimotor polyneuropathy in another patient. The GAA expansion-negative 11-year-old female patient had mental-motor retardation, epilepsy, and ataxia. None of the patients had significant cardiac symptoms. Description of FRDA families with different ethnic backgrounds may assist in identifying possible phenotypic and genetic features of the disease. Furthermore, the genetic heterogeneity observed in this family draws attention to the difficulty of genetic counseling in an inbred population and to the need for genotyping all affected members before delivering comprehensive genetic counseling. PMID:27668106

  18. Prenatal features of Pena-Shokeir sequence with atypical response to acoustic stimulation.

    PubMed

    Pittyanont, Sirida; Jatavan, Phudit; Suwansirikul, Songkiat; Tongsong, Theera

    2016-09-01

    A fetal sonographic screening examination performed at 23 weeks showed polyhydramnios, micrognathia, fixed postures of all long bones, but no movement and no breathing. The fetus showed fetal heart rate acceleration but no movement when acoustic stimulation was applied with artificial larynx. All these findings persisted on serial examinations. The neonate was stillborn at 37 weeks and a final diagnosis of Pena-Shokeir sequence was made. In addition to typical sonographic features of Pena-Shokeir sequence, fetal heart rate accelerations with no movement in response to acoustic stimulation suggests that peripheral myopathy may possibly play an important role in the pathogenesis of the disease. © 2016 Wiley Periodicals, Inc. J Clin Ultrasound 44:459-462, 2016. PMID:27312123

  19. Flexible spatial configuration of local image features.

    PubMed

    Carneiro, Gustavo; Jepson, Allan D

    2007-12-01

    Local image features have been designed to be informative and repeatable under rigid transformations and illumination deformations. Even though current state-of-the-art local image features present a high degree of repeatability, their local appearance alone usually does not bring enough discriminative power to support a reliable matching, resulting in a relatively high number of mismatches in the correspondence set formed during the data association procedure. As a result, geometric filters, commonly based on global spatial configuration, have been used to reduce this number of mismatches. However, this approach presents a trade off between the effectiveness to reject mismatches and the robustness to non-rigid deformations. In this paper, we propose two geometric filters, based on semilocal spatial configuration of local features, that are designed to be robust to non-rigid deformations and to rigid transformations, without compromising its efficacy to reject mismatches. We compare our methods to the Hough transform, which is an efficient and effective mismatch rejection step based on global spatial configuration of features. In these comparisons, our methods are shown to be more effective in the task of rejecting mismatches for rigid transformations and non-rigid deformations at comparable time complexity figures. Finally, we demonstrate how to integrate these methods in a probabilistic recognition system such that the final verification step uses not only the similarity between features, but also their semi-local configuration.

  20. Imaging features of spinal tanycytic ependymoma.

    PubMed

    Tomek, Michal; Jayajothi, Anandapadmanabhan; Brandner, Sebastian; Jaunmuktane, Zane; Lee, Cheong Hung; Davagnanam, Indran

    2016-02-01

    Tanycytic ependymoma is an unusual morphological variant of WHO grade II ependymoma, typically arising from the cervical or thoracic spinal cord. Although the literature deals extensively with pathological features of this tumour entity, imaging features have not been well characterised. The purpose of this study was to review magnetic resonance imaging (MRI) features of spinal tanycytic ependymomas reported in the literature to date, exemplified by a case of a patient with tanycytic ependymoma of the conus medullaris presenting to our hospital. A Medline search of the English literature for all previously published cases of spinal tanycytic ependymoma was carried out and the reported MRI features reviewed. The tumours were found to be typically well-demarcated masses, predominantly showing isointensity on T1-weighted signal, and T2-weighted hyperintensity, with variable patterns of contrast enhancement. A cystic component was seen in half of the cases, and in a minority a mural nodule was present within the cyst wall. Associated syrinx formation was observed in one-third of the cases and haemorrhage was rare, which may be helpful pointers in differentiating the lesion from other ependymoma subtypes. In conclusion, MRI characteristics of spinal tanycytic ependymoma are variable and non-specific, and radiological diagnosis thus remains challenging, although certain predominant features are identified in this report. Knowledge of these is important in the diagnostic differentiation from other intramedullary and extramedullary spinal tumours in order to guide appropriate surgical management. PMID:26755489

  1. A rare case of atypical skull base meningioma with perineural spread

    PubMed Central

    Walton, Henry; Morley, Simon; Alegre-Abarrategui, Javier

    2015-01-01

    Atypical meningioma is a rare cause of perineural tumour spread. In this report, we present the case of a 46-year-old female with an atypical meningioma of the skull base demonstrating perineural tumour spread. We describe the imaging features of this condition and its distinguishing features from other tumours exhibiting perineural spread. PMID:27200171

  2. Early-onset facioscapulohumeral muscular dystrophy type 1 with some atypical features.

    PubMed

    Dorobek, Małgorzata; van der Maarel, Silvère M; Lemmers, Richard J L F; Ryniewicz, Barbara; Kabzińska, Dagmara; Frants, Rune R; Gawel, Malgorzata; Walecki, Jerzy; Hausmanowa-Petrusewicz, Irena

    2015-04-01

    Facioscapulohumeral muscular dystrophy cases with facial weakness before the age of 5 and signs of shoulder weakness by the age of 10 are defined as early onset. Contraction of the D4Z4 repeat on chromosome 4q35 is causally related to facioscapulohumeral muscular dystrophy type 1, and the residual size of the D4Z4 repeat shows a roughly inverse correlation with the severity of the disease. Contraction of the D4Z4 repeat on chromosome 4q35 is believed to induce a local change in chromatin structure and consequent transcriptional deregulation of 4qter genes. We present early-onset cases in the Polish population that amounted to 21% of our total population with facioscapulohumeral muscular dystrophy. More than 27% of them presented with severe phenotypes (wheelchair dependency). The residual D4Z4 repeat sizes ranged from 1 to 4 units. In addition, even within early-onset facioscapulohumeral muscular dystrophy type 1 phenotypes, some cases had uncommon features (head drop, early disabling contractures, progressive ptosis, and respiratory insufficiency and cardiomyopathy).

  3. Atypical Activation during the Embedded Figures Task as a Functional Magnetic Resonance Imaging Endophenotype of Autism

    ERIC Educational Resources Information Center

    Spencer, Michael D.; Holt, Rosemary J.; Chura, Lindsay R.; Calder, Andrew J.; Suckling, John; Bullmore, Edward T.; Baron-Cohen, Simon

    2012-01-01

    Atypical activation during the Embedded Figures Task has been demonstrated in autism, but has not been investigated in siblings or related to measures of clinical severity. We identified atypical activation during the Embedded Figures Task in participants with autism and unaffected siblings compared with control subjects in a number of temporal…

  4. Special feature on imaging systems and techniques

    NASA Astrophysics Data System (ADS)

    Yang, Wuqiang; Giakos, George

    2013-07-01

    The IEEE International Conference on Imaging Systems and Techniques (IST'2012) was held in Manchester, UK, on 16-17 July 2012. The participants came from 26 countries or regions: Austria, Brazil, Canada, China, Denmark, France, Germany, Greece, India, Iran, Iraq, Italy, Japan, Korea, Latvia, Malaysia, Norway, Poland, Portugal, Sweden, Switzerland, Taiwan, Tunisia, UAE, UK and USA. The technical program of the conference consisted of a series of scientific and technical sessions, exploring physical principles, engineering and applications of new imaging systems and techniques, as reflected by the diversity of the submitted papers. Following a rigorous review process, a total of 123 papers were accepted, and they were organized into 30 oral presentation sessions and a poster session. In addition, six invited keynotes were arranged. The conference not only provided the participants with a unique opportunity to exchange ideas and disseminate research outcomes but also paved a way to establish global collaboration. Following the IST'2012, a total of 55 papers, which were technically extended substantially from their versions in the conference proceeding, were submitted as regular papers to this special feature of Measurement Science and Technology . Following a rigorous reviewing process, 25 papers have been finally accepted for publication in this special feature and they are organized into three categories: (1) industrial tomography, (2) imaging systems and techniques and (3) image processing. These papers not only present the latest developments in the field of imaging systems and techniques but also offer potential solutions to existing problems. We hope that this special feature provides a good reference for researchers who are active in the field and will serve as a catalyst to trigger further research. It has been our great pleasure to be the guest editors of this special feature. We would like to thank the authors for their contributions, without which it would

  5. Bone feature analysis using image processing techniques.

    PubMed

    Liu, Z Q; Austin, T; Thomas, C D; Clement, J G

    1996-01-01

    In order to establish the correlation between bone structure and age, and information about age-related bone changes, it is necessary to study microstructural features of human bone. Traditionally, in bone biology and forensic science, the analysis if bone cross-sections has been carried out manually. Such a process is known to be slow, inefficient and prone to human error. Consequently, the results obtained so far have been unreliable. In this paper we present a new approach to quantitative analysis of cross-sections of human bones using digital image processing techniques. We demonstrate that such a system is able to extract various bone features consistently and is capable of providing more reliable data and statistics for bones. Consequently, we will be able to correlate features of bone microstructure with age and possibly also with age related bone diseases such as osteoporosis. The development of knowledge-based computer vision-systems for automated bone image analysis can now be considered feasible.

  6. Atypical activation during the Embedded Figures Task as a functional magnetic resonance imaging endophenotype of autism.

    PubMed

    Spencer, Michael D; Holt, Rosemary J; Chura, Lindsay R; Calder, Andrew J; Suckling, John; Bullmore, Edward T; Baron-Cohen, Simon

    2012-11-01

    Atypical activation during the Embedded Figures Task has been demonstrated in autism, but has not been investigated in siblings or related to measures of clinical severity. We identified atypical activation during the Embedded Figures Task in participants with autism and unaffected siblings compared with control subjects in a number of temporal and frontal brain regions. Autism and sibling groups, however, did not differ in terms of activation during this task. This suggests that the pattern of atypical activation identified may represent a functional endophenotype of autism, related to familial risk for the condition shared between individuals with autism and their siblings. We also found that reduced activation in autism relative to control subjects in regions including associative visual and face processing areas was strongly correlated with the clinical severity of impairments in reciprocal social interaction. Behavioural performance was intact in autism and sibling groups. Results are discussed in terms of atypical information processing styles or of increased activation in temporal and frontal regions in autism and the broader phenotype. By separating the aspects of atypical activation as markers of familial risk for the condition from those that are autism-specific, our findings offer new insight into the factors that might cause the expression of autism in families, affecting some children but not others.

  7. Imaging features of rhinosporidiosis on contrast CT

    PubMed Central

    Prabhu, Shailesh M; Irodi, Aparna; Khiangte, Hannah L; Rupa, V; Naina, P

    2013-01-01

    Context: Rhinosporidiosis is a chronic granulomatous disease endemic in certain regions of India. Computed tomography (CT) imaging appearances of rhinosporidiosis have not been previously described in the literature. Aims: To study imaging features in rhinosporidiosis with contrast-enhanced CT and elucidate its role in the evaluation of this disease. Materials and Methods: Sixteen patients with pathologically proven rhinosporidiosis were included in the study. Contrast-enhanced CT images were analyzed retrospectively and imaging findings were correlated with surgical and histopathologic findings. Results: A total of 29 lesions were found and evaluated. On contrast-enhanced CT, rhinosporidiosis was seen as moderately enhancing lobulated or irregular soft tissue mass lesions in the nasal cavity (n = 13), lesions arising in nasal cavity and extending through choana into nasopharynx (n = 5), pedunculated polypoidal lesions arising from the nasopharyngeal wall (n = 5), oropharyngeal wall (n = 2), larynx (n = 1), bronchus (n = 1), skin and subcutaneous tissue (n = 2). The inferior nasal cavity comprising nasal floor, inferior turbinate, and inferior meatus was the most common site of involvement (n = 13). Surrounding bone involvement was seen in the form of rarefaction (n = 6), partial (n = 3) or complete erosion (n = 3) of inferior turbinate, thinning of medial maxillary wall (n = 2), and septal erosion (n = 2). Nasolacrimal duct involvement was seen in four cases. Conclusions: Contrast-enhanced CT has an important role in delineating the site and extent of the disease, as well as the involvement of surrounding bone, nasolacrimal duct and tracheobronchial tree. This provides a useful roadmap prior to surgery. PMID:24347850

  8. Adhesion, Biofilm Formation, and Genomic Features of Campylobacter jejuni Bf, an Atypical Strain Able to Grow under Aerobic Conditions.

    PubMed

    Bronnec, Vicky; Turoňová, Hana; Bouju, Agnès; Cruveiller, Stéphane; Rodrigues, Ramila; Demnerova, Katerina; Tresse, Odile; Haddad, Nabila; Zagorec, Monique

    2016-01-01

    Campylobacter jejuni is the leading cause of bacterial enteritis in Europe. Human campylobacteriosis cases are frequently associated to the consumption of contaminated poultry meat. To survive under environmental conditions encountered along the food chain, i.e., from poultry digestive tract its natural reservoir to the consumer's plate, this pathogen has developed adaptation mechanisms. Among those, biofilm lifestyle has been suggested as a strategy to survive in the food environment and under atmospheric conditions. Recently, the clinical isolate C. jejuni Bf has been shown to survive and grow under aerobic conditions, a property that may help this strain to better survive along the food chain. The aim of this study was to evaluate the adhesion capacity of C. jejuni Bf and its ability to develop a biofilm. C. jejuni Bf can adhere to abiotic surfaces and to human epithelial cells, and can develop biofilm under both microaerobiosis and aerobiosis. These two conditions have no influence on this strain, unlike results obtained with the reference strain C. jejuni 81-176, which harbors only planktonic cells under aerobic conditions. Compared to 81-176, the biofilm of C. jejuni Bf is more homogenous and cell motility at the bottom of biofilm was not modified whatever the atmosphere used. C. jejuni Bf whole genome sequence did not reveal any gene unique to this strain, suggesting that its unusual property does not result from acquisition of new genetic material. Nevertheless some genetic particularities seem to be shared only between Bf and few others strains. Among the main features of C. jejuni Bf genome we noticed (i) a complete type VI secretion system important in pathogenicity and environmental adaptation; (ii) a mutation in the oorD gene involved in oxygen metabolism; and (iii) the presence of an uncommon insertion of a 72 amino acid coding sequence upstream from dnaK, which is involved in stress resistance. Therefore, the atypical behavior of this strain under

  9. Adhesion, Biofilm Formation, and Genomic Features of Campylobacter jejuni Bf, an Atypical Strain Able to Grow under Aerobic Conditions

    PubMed Central

    Bronnec, Vicky; Turoňová, Hana; Bouju, Agnès; Cruveiller, Stéphane; Rodrigues, Ramila; Demnerova, Katerina; Tresse, Odile; Haddad, Nabila; Zagorec, Monique

    2016-01-01

    Campylobacter jejuni is the leading cause of bacterial enteritis in Europe. Human campylobacteriosis cases are frequently associated to the consumption of contaminated poultry meat. To survive under environmental conditions encountered along the food chain, i.e., from poultry digestive tract its natural reservoir to the consumer’s plate, this pathogen has developed adaptation mechanisms. Among those, biofilm lifestyle has been suggested as a strategy to survive in the food environment and under atmospheric conditions. Recently, the clinical isolate C. jejuni Bf has been shown to survive and grow under aerobic conditions, a property that may help this strain to better survive along the food chain. The aim of this study was to evaluate the adhesion capacity of C. jejuni Bf and its ability to develop a biofilm. C. jejuni Bf can adhere to abiotic surfaces and to human epithelial cells, and can develop biofilm under both microaerobiosis and aerobiosis. These two conditions have no influence on this strain, unlike results obtained with the reference strain C. jejuni 81-176, which harbors only planktonic cells under aerobic conditions. Compared to 81-176, the biofilm of C. jejuni Bf is more homogenous and cell motility at the bottom of biofilm was not modified whatever the atmosphere used. C. jejuni Bf whole genome sequence did not reveal any gene unique to this strain, suggesting that its unusual property does not result from acquisition of new genetic material. Nevertheless some genetic particularities seem to be shared only between Bf and few others strains. Among the main features of C. jejuni Bf genome we noticed (i) a complete type VI secretion system important in pathogenicity and environmental adaptation; (ii) a mutation in the oorD gene involved in oxygen metabolism; and (iii) the presence of an uncommon insertion of a 72 amino acid coding sequence upstream from dnaK, which is involved in stress resistance. Therefore, the atypical behavior of this strain under

  10. An open-label, rater-blinded, 8-week trial of bupropion hydrochloride extended-release in patients with major depressive disorder with atypical features.

    PubMed

    Seo, H-J; Lee, B C; Seok, J-H; Jeon, H J; Paik, J-W; Kim, W; Kwak, K-P; Han, C; Lee, K-U; Pae, C-U

    2013-09-01

    The present study aimed at investigating the effectiveness and tolerability of -bupropion hydrochloride extended release (XL) in major depressive disorder (MDD) patients with atypical features (AF).51 patients were prescribed bupropion XL for 8 weeks (6 visits: screening, baseline, weeks 1, 2, 4 and 8). The primary efficacy measure was a change of the Structured Interview Guide for the Hamilton Depression Rating Scale-Seasonal Affective Disorder Version (SIGH-SAD) from baseline to endpoint. Secondary efficacy measures included the SIGH-SAD atypical symptoms subscale, Clinical Global Impression-Severity (CGI-S), Sheehan Disability Scale (SDS) and Epworth Sleepiness Questionnaire (ESQ). Response or remission was defined as ≥50% reduction or ≤7 in SIGH-SAD total scores, respectively, at end of treatment.The HAM-D-29 total score reduced by 55.3% from baseline (27.3±6.5) to end of treatment (12.2±6.3) (p<0.001). Atypical symptom subscale scores also reduced by 54.5% from baseline (9.2±3.0) to end of treatment (4.2±2.8) (p<0.001). At the end of treatment, 24.4% (n=10) and 51.2% (n=21) subjects were classified as remitters and responders, respectively. The most frequently reported AEs were headache (13.7%), dry mouth (11.8%), dizziness (9.8%), and dyspepsia (9.8%).Our preliminary study indicates that bupropion XL may be beneficial in the treatment of MDD with atypical features. Adequately powered, randomized, double-blind, placebo-controlled trials are necessary to determine our results.

  11. Bilateral Persistent Sciatic Artery Aneurysm Discovered by Atypical Sciatica: A Case Report

    SciTech Connect

    Mazet, Nathalie; Soulier-Guerin, Karine; Ruivard, Marc; Garcier, Jean-Marc; Boyer, Louis

    2006-12-15

    We report a case of a bilateral persistent sciatic artery aneurysm, diagnosed by atypical sciatica on computed tomography and magnetic resonance imaging. The different variants, the revealing features, and possible treatment are discussed.

  12. Image feature extraction based multiple ant colonies cooperation

    NASA Astrophysics Data System (ADS)

    Zhang, Zhilong; Yang, Weiping; Li, Jicheng

    2015-05-01

    This paper presents a novel image feature extraction algorithm based on multiple ant colonies cooperation. Firstly, a low resolution version of the input image is created using Gaussian pyramid algorithm, and two ant colonies are spread on the source image and low resolution image respectively. The ant colony on the low resolution image uses phase congruency as its inspiration information, while the ant colony on the source image uses gradient magnitude as its inspiration information. These two ant colonies cooperate to extract salient image features through sharing a same pheromone matrix. After the optimization process, image features are detected based on thresholding the pheromone matrix. Since gradient magnitude and phase congruency of the input image are used as inspiration information of the ant colonies, our algorithm shows higher intelligence and is capable of acquiring more complete and meaningful image features than other simpler edge detectors.

  13. Defects' geometric feature recognition based on infrared image edge detection

    NASA Astrophysics Data System (ADS)

    Junyan, Liu; Qingju, Tang; Yang, Wang; Yumei, Lu; Zhiping, Zhang

    2014-11-01

    Edge detection is an important technology in image segmentation, feature extraction and other digital image processing areas. Boundary contains a wealth of information in the image, so to extract defects' edges in infrared images effectively enables the identification of defects' geometric features. This paper analyzed the detection effect of classic edge detection operators, and proposed fuzzy C-means (FCM) clustering-Canny operator algorithm to achieve defects' edges in the infrared images. Results show that the proposed algorithm has better effect than the classic edge detection operators, which can identify the defects' geometric feature much more completely and clearly. The defects' diameters have been calculated based on the image edge detection results.

  14. [Atypical depression].

    PubMed

    Escande, M; Boucard, J

    1999-04-01

    The principal atypical aspects of depressive disease are: minor and attenued aspects, monosymptomatic and atypical aspects (food disorders and sleep disorders), masqued aspects (somatoform, anxious, characterial and addict disorders), atypical aspects of child (anxious nevrotical disorder), pseudo-demented and characterial aspects of aged subjects. Facing to these aspects, the diagnosis of depression is evoqued on: the recent and fast advent of these disorders, their morning predominance, their recurrent character, the state of attenued depressive symptoms (anhedonia), the positive responsiveness to treatment.

  15. Intrinsic feature-based pose measurement for imaging motion compensation

    DOEpatents

    Baba, Justin S.; Goddard, Jr., James Samuel

    2014-08-19

    Systems and methods for generating motion corrected tomographic images are provided. A method includes obtaining first images of a region of interest (ROI) to be imaged and associated with a first time, where the first images are associated with different positions and orientations with respect to the ROI. The method also includes defining an active region in the each of the first images and selecting intrinsic features in each of the first images based on the active region. Second, identifying a portion of the intrinsic features temporally and spatially matching intrinsic features in corresponding ones of second images of the ROI associated with a second time prior to the first time and computing three-dimensional (3D) coordinates for the portion of the intrinsic features. Finally, the method includes computing a relative pose for the first images based on the 3D coordinates.

  16. [Multiple transmission electron microscopic image stitching based on sift features].

    PubMed

    Li, Mu; Lu, Yanmeng; Han, Shuaihu; Wu, Zhuobin; Chen, Jiajing; Liu, Zhexing; Cao, Lei

    2015-08-01

    We proposed a new stitching method based on sift features to obtain an enlarged view of transmission electron microscopic (TEM) images with a high resolution. The sift features were extracted from the images, which were then combined with fitted polynomial correction field to correct the images, followed by image alignment based on the sift features. The image seams at the junction were finally removed by Poisson image editing to achieve seamless stitching, which was validated on 60 local glomerular TEM images with an image alignment error of 62.5 to 187.5 nm. Compared with 3 other stitching methods, the proposed method could effectively reduce image deformation and avoid artifacts to facilitate renal biopsy pathological diagnosis. PMID:26403733

  17. The remote sensing image retrieval based on multi-feature

    NASA Astrophysics Data System (ADS)

    Duan, Jian-bo; Ma, Cai-hong; Liu, Shi-bin; Zhang, Jing

    2013-10-01

    With the rapid development of remote sensing technology and variety of earth observation satellites have been successfully launched, the volume of image datasets is growing exponentially in many application areas. The Contentbased image retrieval (CBRSIR), as an efficient means for management and utilization of the information in image database from the viewpoint of comprehension of image content, is applied on the remote sensing images retrieval. However, one kind of features always can't express the image content exactly. So, a multi-feature retrieval model based on three color features and four texture features is proposed in this paper. The experiment results show that the multifeatures model can improve the retrieval results than other model just by each singular feature.

  18. Scale-invariant features and polar descriptors in omnidirectional imaging.

    PubMed

    Arican, Zafer; Frossard, Pascal

    2012-05-01

    We propose a method to compute scale-invariant features in omnidirectional images. We present a formulation based on the Riemannian geometry for the definition of differential operators on non-Euclidian manifolds that adapt to the mirror and lens structures in omnidirectional imaging. These operators lead to a scale-space analysis that preserves the geometry of the visual information in omnidirectional images. We then build a novel scale-invariant feature detection framework for omnidirectional images that can be mapped on the sphere. We further present a new descriptor and feature matching solution for these omnidirectional images. The descriptor builds on the log-polar planar descriptors and adapts the descriptor computation to the specific geometry and the nonuniform sampling density of omnidirectional images. We also propose a rotation-invariant matching method that eliminates the orientation computation during the feature detection phase and thus decreases the computational complexity. Experimental results demonstrate that the new feature computation method combined with the adapted descriptors offers promising detection and matching performance, i.e., it improves on the common scale-invariant feature transform (SIFT) features computed on the unwrapped omnidirectional images, as well as spherical SIFT features. Finally, we show that the proposed framework also permits to match features between images with different native geometry.

  19. Image sharpness function based on edge feature

    NASA Astrophysics Data System (ADS)

    Jun, Ni

    2009-11-01

    Autofocus technique has been widely used in optical tracking and measure system, but it has problem that when the autofocus device should to work. So, no-reference image sharpness assessment has become an important issue. A new Sharpness Function that can estimate current frame image be in focus or not is proposed in this paper. According to current image whether in focus or not and choose the time of auto focus automatism. The algorithm measures object typical edge and edge direction, and then get image local kurtosis information to determine the degree of image sharpness. It firstly select several grads points cross the edge line, secondly calculates edge sharpness value and get the cure of the kurtosis, according the measure precision of optical-equipment, a threshold value will be set beforehand. If edge kurtosis value is more than threshold, it can conclude current frame image is in focus. Otherwise, it is out of focus. If image is out of focus, optics system then takes autofocus program. This algorithm test several thousands of digital images captured from optical tracking and measure system. The results show high correlation with subjective sharpness assessment for s images of sky object.

  20. Feature-preserving image/video compression

    NASA Astrophysics Data System (ADS)

    Al-Jawad, Naseer; Jassim, Sabah

    2005-10-01

    Advances in digital image processing, the advents of multimedia computing, and the availability of affordable high quality digital cameras have led to increased demand for digital images/videos. There has been a fast growth in the number of information systems that benefit from digital imaging techniques and present many tough challenges. In this paper e are concerned with applications for which image quality is a critical requirement. The fields of medicine, remote sensing, real time surveillance, and image-based automatic fingerprint/face identification systems are all but few examples of such applications. Medical care is increasingly dependent on imaging for diagnostics, surgery, and education. It is estimated that medium size hospitals in the US generate terabytes of MRI images and X-Ray images are generated to be stored in very large databases which are frequently accessed and searched for research and training. On the other hand, the rise of international terrorism and the growth of identity theft have added urgency to the development of new efficient biometric-based person verification/authentication systems. In future, such systems can provide an additional layer of security for online transactions or for real-time surveillance.

  1. Registration of multitemporal aerial optical images using line features

    NASA Astrophysics Data System (ADS)

    Zhao, Chenyang; Goshtasby, A. Ardeshir

    2016-07-01

    Registration of multitemporal images is generally considered difficult because scene changes can occur between the times the images are obtained. Since the changes are mostly radiometric in nature, features are needed that are insensitive to radiometric differences between the images. Lines are geometric features that represent straight edges of rigid man-made structures. Because such structures rarely change over time, lines represent stable geometric features that can be used to register multitemporal remote sensing images. An algorithm to establish correspondence between lines in two images of a planar scene is introduced and formulas to relate the parameters of a homography transformation to the parameters of corresponding lines in images are derived. Results of the proposed image registration on various multitemporal images are presented and discussed.

  2. Atypical Mole (Atypical Nevus)

    MedlinePlus

    ... 2006-2013 Logical Images, Inc. All rights reserved. Advertising Notice This Site and third parties who place ... would like to obtain more information about these advertising practices and to make choices about online behavioral ...

  3. Efficient vessel feature detection for endoscopic image analysis.

    PubMed

    Lin, Bingxiong; Sun, Yu; Sanchez, Jaime E; Qian, Xiaoning

    2015-04-01

    Distinctive feature detection is an essential task in computer-assisted minimally invasive surgery (MIS). For special conditions in an MIS imaging environment, such as specular reflections and texture homogeneous areas, the feature points extracted by general feature point detectors are less distinctive and repeatable in MIS images. We observe that abundant blood vessels are available on tissue surfaces and can be extracted as a new set of image features. In this paper, two types of blood vessel features are proposed for endoscopic images: branching points and branching segments. Two novel methods, ridgeness-based circle test and ridgeness-based branching segment detection are presented to extract branching points and branching segments, respectively. Extensive in vivo experiments were conducted to evaluate the performance of the proposed methods and compare them with the state-of-the-art methods. The numerical results verify that, in MIS images, the blood vessel features can produce a large number of points.More importantly, those points are more robust and repeatable than the other types of feature points. In addition, due to the difference in feature types, vessel features can be combined with other general features, which makes them new tools for MIS image analysis. These proposed methods are efficient and the code and datasets are made available to the public.

  4. Remote sensing image classification based on block feature point density analysis and multiple-feature fusion

    NASA Astrophysics Data System (ADS)

    Li, Shijin; Jiang, Yaping; Zhang, Yang; Feng, Jun

    2015-10-01

    With the development of remote sensing (RS) and the related technologies, the resolution of RS images is enhancing. Compared with moderate or low resolution images, high-resolution ones can provide more detailed ground information. However, a variety of terrain has complex spatial distribution. The different objectives of high-resolution images have a variety of features. The effectiveness of these features is not the same, but some of them are complementary. Considering the above information and characteristics, a new method is proposed to classify RS images based on hierarchical fusion of multi-features. Firstly, RS images are pre-classified into two categories in terms of whether feature points are uniformly or non-uniformly distributed. Then, the color histogram and Gabor texture feature are extracted from the uniformly-distributed categories, and the linear spatial pyramid matching using sparse coding (ScSPM) feature is obtained from the non-uniformly-distributed categories. Finally, the classification is performed by two support vector machine classifiers. The experimental results on a large RS image database with 2100 images show that the overall classification accuracy is boosted by 10.1% in comparison with the highest accuracy of single feature classification method. Compared with other multiple-feature fusion methods, the proposed method has achieved the highest classification accuracy on this dataset which has reached 90.1%, and the time complexity of the algorithm is also greatly reduced.

  5. Detecting Image Splicing Using Merged Features in Chroma Space

    PubMed Central

    Liu, Guangjie; Dai, Yuewei

    2014-01-01

    Image splicing is an image editing method to copy a part of an image and paste it onto another image, and it is commonly followed by postprocessing such as local/global blurring, compression, and resizing. To detect this kind of forgery, the image rich models, a feature set successfully used in the steganalysis is evaluated on the splicing image dataset at first, and the dominant submodel is selected as the first kind of feature. The selected feature and the DCT Markov features are used together to detect splicing forgery in the chroma channel, which is convinced effective in splicing detection. The experimental results indicate that the proposed method can detect splicing forgeries with lower error rate compared to the previous literature. PMID:24574877

  6. Subpixel measurement of image features based on paraboloid surface fit

    SciTech Connect

    Gleason, S.S.; Hunt, M.A.; Jatko, W.B.

    1990-01-01

    A digital image processing inspection system is under development at Oak Ridge National Laboratory that will locate image features on printed material and measure distances between them to accuracies of 0.001 in. An algorithm has been developed for this system that can locate unique image features to subpixel accuracies. It is based on a least-squares fit of a paraboloid function to the surface generated by correlating a reference image feature against a test image search area. normalizing the correlation surface makes the algorithm robust in the presence of illumination variations and local flaws. Subpixel accuracies better than 1/16 of a pixel have been achieved using a variety of different reference image features. 5 refs., 6 figs.

  7. Giant Cell Arteritis: An Atypical Presentation Diagnosed with the Use of MRI Imaging

    PubMed Central

    2016-01-01

    Giant cell arteritis (GCA) is the most common primary systemic vasculitis in western countries in individuals over the age of 50. It is typically characterised by the granulomatous involvement of large and medium sized blood vessels branching of the aorta with particular tendencies for involving the extracranial branches of the carotid artery. Generally the diagnosis is straightforward when characteristic symptoms such as headache, jaw claudication, or other ischemic complications are present. Atypical presentations of GCA without “overt” cranial ischemic manifestations have become increasingly recognised but we report for the first time a case of GCA presenting as mild upper abdominal pain and generalized weakness in the context of hyponatremia as the presenting manifestation of vasculitis that was subsequently diagnosed by MRI scanning. This case adds to the literature and emphasises the importance of MRI in the evaluation of GCA patients without “classic” cranial ischemic symptoms. PMID:27493825

  8. Magnetic Resonance Imaging Findings of Early Spondylodiscitis: Interpretive Challenges and Atypical Findings

    PubMed Central

    Yeom, Jeong A; Suh, Hie Bum; Song, You Seon; Song, Jong Woon

    2016-01-01

    MR findings of early infectious spondylodiscitis are non-specific and may be confused with those of other conditions. Therefore, it is important to recognize early MR signs of conditions, such as inappreciable cortical changes in endplates, confusing marrow signal intensities of vertebral bodies, and inflammatory changes in paraspinal soft tissues, and subligamentous and epidural spaces. In addition, appreciation of direct inoculation, such as in iatrogenic spondylodiscitis may be important, because the proportion of patients who have undergone recent spine surgery or a spinal procedure is increasing. In this review, the authors focus on the MR findings of early spondylodiscitis, atypical findings of iatrogenic infection, and the differentiation between spondylodiscitis and other disease entities mimicking infection. PMID:27587946

  9. Magnetic Resonance Imaging Findings of Early Spondylodiscitis: Interpretive Challenges and Atypical Findings.

    PubMed

    Yeom, Jeong A; Lee, In Sook; Suh, Hie Bum; Song, You Seon; Song, Jong Woon

    2016-01-01

    MR findings of early infectious spondylodiscitis are non-specific and may be confused with those of other conditions. Therefore, it is important to recognize early MR signs of conditions, such as inappreciable cortical changes in endplates, confusing marrow signal intensities of vertebral bodies, and inflammatory changes in paraspinal soft tissues, and subligamentous and epidural spaces. In addition, appreciation of direct inoculation, such as in iatrogenic spondylodiscitis may be important, because the proportion of patients who have undergone recent spine surgery or a spinal procedure is increasing. In this review, the authors focus on the MR findings of early spondylodiscitis, atypical findings of iatrogenic infection, and the differentiation between spondylodiscitis and other disease entities mimicking infection. PMID:27587946

  10. Virulence features of atypical enteropathogenic Escherichia coli identified by the eae(+) EAF-negative stx(-) genetic profile.

    PubMed

    Abe, Cecilia M; Trabulsi, Luiz R; Blanco, Jorge; Blanco, Miguel; Dahbi, Ghizlane; Blanco, Jesús E; Mora, Azucena; Franzolin, Marcia R; Taddei, Carla R; Martinez, Marina B; Piazza, Roxane M F; Elias, Waldir P

    2009-08-01

    This study characterized 76 atypical enteropathogenic Escherichia coli (aEPEC) strains, previously classified by the eae(+) EAF-negative stx(-) genotype, isolated from children with diarrhea in Brazil. Presence of bfpA and bfpA/perA was detected in 2 and 6 strains, respectively. The expression of bundle-forming pilus (BFP), however, was observed by immunofluorescence in 1 bfpA and 3 bfpA/perA strains, classifying them as typical EPEC (tEPEC). The remaining 72 aEPEC strains were characterized by serotyping, intimin typing, adherence patterns to HEp-2 cells, capacity to induce actin aggregation (fluorescent actin staining test), and antimicrobial resistance. Our results show that aEPEC comprise a very heterogeneous group that does not present any prevalence or association regarding the studied characteristics. It also suggest that tEPEC and aEPEC must not be classified only by the reactivity with the EAF probe, and that the search of other markers present in pEAF, as well as the BFP expression, must be considered for this matter.

  11. Featured Image: The Milky Way's X

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2016-09-01

    The X-shaped bulge is even more evident in this image, wherein a simple exponential disk model has been subtracted off. [Adapted from Ness Lang 2016]This contrast-enhanced image of the Milky Way, observed by the Wide-Field Infrared Survey Explorer (WISE), clearly reveals that the bulge of stars at the center of our galaxy is shaped like a large X. The boxy nature of the Milky Ways bulge was revealed by satellite image in 1995, but in recent years, star counts along the line of sight toward the bulge have suggested that the bulge may be X-shaped. It was unclear whether this apparent morphology was due to the difference in the distributions of different stellar populations, or if the actual physical structure of the bulge was X-shaped. But these new WISE images, produced by astronomers Melissa Ness (Max Planck Institute for Astronomy) and Dustin Lang (University of Toronto and University of Waterloo), now provide firm evidence that the Milky Ways bulge actually is X-shaped, supplying clues as to how our galaxys center may have formed. This morphology is not uncommon; observations of other barred galaxies reveal similar X-shaped profiles. To learn more, check out the paper below!CitationMelissa Ness and Dustin Lang 2016 AJ 152 14. doi:10.3847/0004-6256/152/1/14

  12. Automated Recognition of 3D Features in GPIR Images

    NASA Technical Reports Server (NTRS)

    Park, Han; Stough, Timothy; Fijany, Amir

    2007-01-01

    A method of automated recognition of three-dimensional (3D) features in images generated by ground-penetrating imaging radar (GPIR) is undergoing development. GPIR 3D images can be analyzed to detect and identify such subsurface features as pipes and other utility conduits. Until now, much of the analysis of GPIR images has been performed manually by expert operators who must visually identify and track each feature. The present method is intended to satisfy a need for more efficient and accurate analysis by means of algorithms that can automatically identify and track subsurface features, with minimal supervision by human operators. In this method, data from multiple sources (for example, data on different features extracted by different algorithms) are fused together for identifying subsurface objects. The algorithms of this method can be classified in several different ways. In one classification, the algorithms fall into three classes: (1) image-processing algorithms, (2) feature- extraction algorithms, and (3) a multiaxis data-fusion/pattern-recognition algorithm that includes a combination of machine-learning, pattern-recognition, and object-linking algorithms. The image-processing class includes preprocessing algorithms for reducing noise and enhancing target features for pattern recognition. The feature-extraction algorithms operate on preprocessed data to extract such specific features in images as two-dimensional (2D) slices of a pipe. Then the multiaxis data-fusion/ pattern-recognition algorithm identifies, classifies, and reconstructs 3D objects from the extracted features. In this process, multiple 2D features extracted by use of different algorithms and representing views along different directions are used to identify and reconstruct 3D objects. In object linking, which is an essential part of this process, features identified in successive 2D slices and located within a threshold radius of identical features in adjacent slices are linked in a

  13. Computation of morphological texture features for medical imaging applications

    NASA Astrophysics Data System (ADS)

    Patel, Manish J.; Kehtarnavaz, Nasser; Dougherty, Edward R.; Batman, Sinan; Sivakumar, Krishnamoorthy; Popov, Antony T.

    1998-06-01

    Texture is an important attribute which is widely used in various image analysis applications. Among texture features, morphological texture features are least utilized in medical image analysis. From a computational standpoint, extracting morphological texture features from an image is a challenging task. The computational problem is made even greater in medical imaging applications where large images such as mammograms are to be analyzed. This paper discusses an efficient method to compute morphological texture features for any geometry of a structuring element corresponding to a texture type. A benchmarking of the code on three machines (Sun SPARC 20, Pentium II based Dell 400 workstation, and SGI Power Challenge 10000XL) as well as a parallel processing implementation was performed to obtain an optimum processing configuration. A sample processed mammogram is shown to illustrate the code outcome.

  14. Image counter-forensics based on feature injection

    NASA Astrophysics Data System (ADS)

    Iuliani, M.; Rossetto, S.; Bianchi, T.; De Rosa, Alessia; Piva, A.; Barni, M.

    2014-02-01

    Starting from the concept that many image forensic tools are based on the detection of some features revealing a particular aspect of the history of an image, in this work we model the counter-forensic attack as the injection of a specific fake feature pointing to the same history of an authentic reference image. We propose a general attack strategy that does not rely on a specific detector structure. Given a source image x and a target image y, the adversary processes x in the pixel domain producing an attacked image ~x, perceptually similar to x, whose feature f(~x) is as close as possible to f(y) computed on y. Our proposed counter-forensic attack consists in the constrained minimization of the feature distance Φ(z) =│ f(z) - f(y)│ through iterative methods based on gradient descent. To solve the intrinsic limit due to the numerical estimation of the gradient on large images, we propose the application of a feature decomposition process, that allows the problem to be reduced into many subproblems on the blocks the image is partitioned into. The proposed strategy has been tested by attacking three different features and its performance has been compared to state-of-the-art counter-forensic methods.

  15. Model Based Analysis of Face Images for Facial Feature Extraction

    NASA Astrophysics Data System (ADS)

    Riaz, Zahid; Mayer, Christoph; Beetz, Michael; Radig, Bernd

    This paper describes a comprehensive approach to extract a common feature set from the image sequences. We use simple features which are easily extracted from a 3D wireframe model and efficiently used for different applications on a benchmark database. Features verstality is experimented on facial expressions recognition, face reognition and gender classification. We experiment different combinations of the features and find reasonable results with a combined features approach which contain structural, textural and temporal variations. The idea follows in fitting a model to human face images and extracting shape and texture information. We parametrize these extracted information from the image sequences using active appearance model (AAM) approach. We further compute temporal parameters using optical flow to consider local feature variations. Finally we combine these parameters to form a feature vector for all the images in our database. These features are then experimented with binary decision tree (BDT) and Bayesian Network (BN) for classification. We evaluated our results on image sequences of Cohn Kanade Facial Expression Database (CKFED). The proposed system produced very promising recognition rates for our applications with same set of features and classifiers. The system is also realtime capable and automatic.

  16. Simple Low Level Features for Image Analysis

    NASA Astrophysics Data System (ADS)

    Falcoz, Paolo

    As human beings, we perceive the world around us mainly through our eyes, and give what we see the status of “reality”; as such we historically tried to create ways of recording this reality so we could augment or extend our memory. From early attempts in photography like the image produced in 1826 by the French inventor Nicéphore Niépce (Figure 2.1) to the latest high definition camcorders, the number of recorded pieces of reality increased exponentially, posing the problem of managing all that information. Most of the raw video material produced today has lost its memory augmentation function, as it will hardly ever be viewed by any human; pervasive CCTVs are an example. They generate an enormous amount of data each day, but there is not enough “human processing power” to view them. Therefore the need for effective automatic image analysis tools is great, and a lot effort has been put in it, both from the academia and the industry. In this chapter, a review of some of the most important image analysis tools are presented.

  17. Shearlet Features for Registration of Remotely Sensed Multitemporal Images

    NASA Technical Reports Server (NTRS)

    Murphy, James M.; Le Moigne, Jacqueline

    2015-01-01

    We investigate the role of anisotropic feature extraction methods for automatic image registration of remotely sensed multitemporal images. Building on the classical use of wavelets in image registration, we develop an algorithm based on shearlets, a mathematical generalization of wavelets that offers increased directional sensitivity. Initial experimental results on LANDSAT images are presented, which indicate superior performance of the shearlet algorithm when compared to classical wavelet algorithms.

  18. Adaptive enhancement method of infrared image based on scene feature

    NASA Astrophysics Data System (ADS)

    Zhang, Xiao; Bai, Tingzhu; Shang, Fei

    2008-12-01

    All objects emit radiation in amounts related to their temperature and their ability to emit radiation. The infrared image shows the invisible infrared radiation emitted directly. Because of the advantages, the technology of infrared imaging is applied to many kinds of fields. But compared with visible image, the disadvantages of infrared image are obvious. The characteristics of low luminance, low contrast and the inconspicuous difference target and background are the main disadvantages of infrared image. The aim of infrared image enhancement is to improve the interpretability or perception of information in infrared image for human viewers, or to provide 'better' input for other automated image processing techniques. Most of the adaptive algorithm for image enhancement is mainly based on the gray-scale distribution of infrared image, and is not associated with the actual image scene of the features. So the pertinence of infrared image enhancement is not strong, and the infrared image is not conducive to the application of infrared surveillance. In this paper we have developed a scene feature-based algorithm to enhance the contrast of infrared image adaptively. At first, after analyzing the scene feature of different infrared image, we have chosen the feasible parameters to describe the infrared image. In the second place, we have constructed the new histogram distributing base on the chosen parameters by using Gaussian function. In the last place, the infrared image is enhanced by constructing a new form of histogram. Experimental results show that the algorithm has better performance than other methods mentioned in this paper for infrared scene images.

  19. Effect of in utero exposure to the atypical anti-psychotic risperidone on histopathological features of the rat placenta.

    PubMed

    Singh, K P; Singh, Manoj K; Gautam, Shrikant

    2016-04-01

    For clinical management of different forms of psychosis, both classical and atypical anti-psychotic drugs (APDs) are available. These drugs are widely prescribed, even during pregnancy considering their minimal extra-pyramidal side effects and teratogenic potential compared to classical APDs. Among AAPDs, risperidone (RIS) is a first-line drug of choice by physicians. The molecular weight of RIS is 410.49 g/mol; hence, it can easily cross the placental barrier and enter the foetal bloodstream. It is not known whether or not AAPDs like RIS may affect the developing placenta and foetus adversely. Reports on this issue are limited and sketchy. Therefore, this study has evaluated the effects of maternal exposure to equivalent therapeutic doses of RIS on placental growth, histopathological and cytoarchitectural changes, and to establish a relationship between placental dysfunction and foetal outcomes. Pregnant rats (n = 24) were exposed to selected doses (0.8, 1.0 and 2.0 mg/kg) of RIS from gestation days 6-21. These dams were sacrificed; their placentas and foetuses were collected, morphometrically examined and further processed for histopathological examination. This study revealed that in utero exposure to equivalent therapeutic doses of RIS during organogenesis-induced placental dystrophy (size and weight), disturbed cytoarchitectural organization (thickness of different placental layers), histopathological lesions (necrosis in trophoblast with disruption of trophoblastic septa and rupturing of maternal-foetal interface) and intrauterine growth restriction of the foetuses. It may be concluded that multifactorial mechanisms might be involved in the dysregulation of structure and function of the placenta and of poor foetal growth and development. PMID:27256515

  20. Atypical pattern of discriminating sound features in adults with Asperger syndrome as reflected by the mismatch negativity.

    PubMed

    Kujala, T; Aho, E; Lepistö, T; Jansson-Verkasalo, E; Nieminen-von Wendt, T; von Wendt, L; Näätänen, R

    2007-04-01

    Asperger syndrome, which belongs to the autistic spectrum of disorders, is characterized by deficits of social interaction and abnormal perception, like hypo- or hypersensitivity in reacting to sounds and discriminating certain sound features. We determined auditory feature discrimination in adults with Asperger syndrome with the mismatch negativity (MMN), a neural response which is an index of cortical change detection. We recorded MMN for five different sound features (duration, frequency, intensity, location, and gap). Our results suggest hypersensitive auditory change detection in Asperger syndrome, as reflected in the enhanced MMN for deviant sounds with a gap or shorter duration, and speeded MMN elicitation for frequency changes.

  1. Clinical Utility of Amyloid PET Imaging in the Differential Diagnosis of Atypical Dementias and Its Impact on Caregivers.

    PubMed

    Bensaïdane, Mohamed Reda; Beauregard, Jean-Mathieu; Poulin, Stéphane; Buteau, François-Alexandre; Guimond, Jean; Bergeron, David; Verret, Louis; Fortin, Marie-Pierre; Houde, Michèle; Bouchard, Rémi W; Soucy, Jean-Paul; Laforce, Robert

    2016-04-18

    Recent studies have supported a role for amyloid positron emission tomography (PET) imaging in distinguishing Alzheimer's disease (AD) pathology from other pathological protein accumulations leading to dementia. We investigated the clinical utility of amyloid PET in the differential diagnosis of atypical dementia cases and its impact on caregivers. Using the amyloid tracer 18F-NAV4694, we prospectively scanned 28 patients (mean age 59.3 y, s.d. 5.8; mean MMSE 21.4, s.d. 6.0) with an atypical dementia syndrome. Following a comprehensive diagnostic workup (i.e., history taking, neurological examination, blood tests, neuropsychological evaluation, MRI, and FDG-PET), no certain diagnosis could be arrived at. Amyloid PET was then conducted and classified as positive or negative. Attending physicians were asked to evaluate whether this result led to a change in diagnosis or altered management. They also reported their degree of confidence in the diagnosis. Caregivers were met after disclosure of amyloid PET results and completed a questionnaire/interview to assess the impact of the scan. Our cohort was evenly divided between positive (14/28) and negative (14/28) 18F-NAV4694 cases. Amyloid PET resulted in a diagnostic change in 9/28 cases (32.1%: 17.8% changed from AD to non-AD, 14.3% from non-AD to AD). There was a 44% increase in diagnostic confidence. Altered management occurred in 71.4% (20/28) of cases. Knowledge of amyloid status improved caregivers' outcomes in all domains (anxiety, depression, disease perception, future anticipation, and quality of life). This study suggests a useful additive role for amyloid PET in atypical cases with an unclear diagnosis beyond the extensive workup of a tertiary memory clinic. Amyloid PET increased diagnostic confidence and led to clinically significant alterations in management. The information gained from that test was well received by caregivers and encouraged spending quality time with their loved ones. PMID:27104896

  2. Intrinsic Feature Pose Measurement for Awake Animal SPECT Imaging

    SciTech Connect

    Goddard Jr, James Samuel; Baba, Justin S; Lee, Seung Joon; Weisenberger, A G; Stolin, A; McKisson, J; Smith, M F

    2009-01-01

    New developments have been made in optical motion tracking for awake animal imaging that measures 3D position and orientation (pose) for a single photon emission computed tomography (SPECT) imaging system. Ongoing SPECT imaging research has been directed towards head motion measurement for brain studies in awake, unrestrained mice. In contrast to previous results using external markers, this work extracts and tracks intrinsic features from multiple camera images and computes relative pose from the tracked features over time. Motion tracking thus far has been limited to measuring extrinsic features such as retro-reflective markers applied to the mouse s head. While this approach has been proven to be accurate, the additional animal handling required to attach the markers is undesirable. A significant improvement in the procedure is achieved by measuring the pose of the head without extrinsic markers using only the external surface appearance. This approach is currently being developed with initial results presented here. The intrinsic features measurement extracts discrete, sparse natural features from 2D images such as eyes, nose, mouth and other visible structures. Stereo correspondence between features for a camera pair is determined for calculation of 3D positions. These features are also tracked over time to provide continuity for surface model fitting. Experimental results from live images are presented.

  3. Tracking image features using a parallel computational model

    NASA Astrophysics Data System (ADS)

    Ellis, Timothy J.; Mirmehdi, Majid; Dowling, Geoff R.

    1992-03-01

    This paper describes a parallel implementation of an image feature tracking system. The system is designed to operate as the front-end of a vision system for controlling autonomous guided vehicles (AGV). Image features or tokens (edge-based line segments in the example given here) are extracted from the image and allocated to individual tracking processes. Both the extraction and the tracking stages are performed by concurrent processes. Arbitrary tracking algorithms may be associated with each process. In the current implementation, a Kalman filter is used to track and predict tokens in subsequent image frames.

  4. The os trigonum syndrome: imaging features.

    PubMed

    Karasick, D; Schweitzer, M E

    1996-01-01

    The os trigonum syndrome refers to symptoms produced by pathology of the lateral tubercle of the posterior talar process. Pain can be caused by disruption of the cartilaginous synchondrosis between the os trigonum and the lateral talar tubercle as a result of repetitive microtrauma and chronic inflammation. Additional etiologies include trigonal process fracture, flexor hallucis longus tenosynovitis, posterior tibiotalar impingement by bone block, and intraarticular loose bodies. This pictorial essay explores the role of imaging modalities in the diagnosis and treatment of the os trigonum syndrome, a symptom complex that may present difficult diagnostic problems.

  5. Featured Image: A Bubble Triggering Star Formation

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2016-05-01

    This remarkable false-color, mid-infrared image (click for the full view!) was produced by the Wide-field Infrared Survey Explorer (WISE). It captures a tantalizing view of Sh 2-207 and Sh 2-208, the latter of which is one of the lowest-metallicity star-forming regions in the Galaxy. In a recent study led by Chikako Yasui (University of Tokyo and the Koyama Astronomical Observatory), a team of scientists has examined this region to better understand how star formation in low-metallicity environments differs from that in the solar neighborhood. The authors analysis suggests that sequential star formation is taking place in these low-metallicity regions, triggered by an expanding bubble (the large dashed oval indicated in the image) with a ~30 pc radius. You can find out more about their study by checking out the paper below!CitationChikako Yasui et al 2016 AJ 151 115. doi:10.3847/0004-6256/151/5/115

  6. Featured Image: Reddened Stars Reveal Andromeda's Dust

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2015-12-01

    As distant light travels on a path toward us, it can be absorbed by intervening, interstellar dust. Much work has been done to understand this dust extinction in the Milky Way, providing us with detailed information about the properties of the dust in our galaxy. Far less, however, is known about the dust extinction of other galaxies. The image above, taken with the ultraviolet space telescope GALEX, identifies the locations of four stars in the nearby Andromeda galaxy (click for a full view!) that are reddened due to extinction of their light by dust within Andromeda. In a recent study led by Geoffrey Clayton (Louisiana State University), new, high-signal-to-noise spectra were obtained for these four stars using Hubbles Space Telescope Imaging Spectrograph. These observations have allowed the authors to construct dust extinction curves to carefully study the nature of Andromedas interstellar dust. To learn about the results, see the paper below.CitationGeoffrey C. Clayton et al 2015 ApJ 815 14. doi:10.1088/0004-637X/815/1/14

  7. An image feature data compressing method based on product RSOM

    NASA Astrophysics Data System (ADS)

    Wang, Jianming; Liu, Lihua; Xia, Shengping

    2015-12-01

    Data explosion and information redundancy are the main characteristics of the era of big data. Digging out valuable information from mass data is the premise of efficient information processing, which is a key technology in the area of object recognition with mass feature database. In the area of large scale image processing, both of the massive image data and the image features of high-dimension take great challenges to object recognition and information retrieval. Similar with big data, the large scale image feature database, which contains extensive quantity of information redundancy, can also be quantitatively represented by finite clustering models without degrading recognition performance. Inspired by the ideas of product quantization and high dimensional feature division, a data compression method based on recursive self-organizing mapping (RSOM) algorithm is proposed in this paper.

  8. Geophysical imaging of karst features in Missouri

    NASA Astrophysics Data System (ADS)

    Obi, Jeremiah Chukwunonso

    Automated electrical resistivity tomography (ERT) supported with multichannel analysis of surface waves (MASW) and boring data were used to map karst related features in Missouri in order to understand karst processes better in Missouri. Previous works on karst in Missouri were mostly surficial mapping of bedrock outcrops and joints, which are not enough to define the internal structure of karst system, since most critical processes in karst occur underground. To understand these processes better, the density, placement and pattern of karst related features like solution-widened joints and voids, as well as top of bedrock were mapped. In the course of the study, six study sites were visited in Missouri. The sites were in Nixa, Gasconade River Bridge in Lebanon, Battlefield, Aurora, Protem and Richland. The case studies reflect to a large extent some of the problems inherent in karst terrain, ranging from environmental problems to structural problems especially sinkhole collapses. The result of the study showed that karst in Missouri is mostly formed as a result of piping of sediments through solution-widened joints, with a pattern showing that the joints/fractures are mostly filled with moist clay-sized materials of low resistivity values. The highest density of mapped solution-widened joints was one in every one hundred and fifty feet, and these areas are where intense dissolution is taking place, and bedrock pervasively fractured. The study also showed that interpreted solution-widened joints trend in different directions, and often times conform with known structural lineaments in the area. About 40% of sinkhole collapses in the study areas are anthropogenic. Karst in Missouri varies, and can be classified as a combination of kI (juvenile), kIII (mature) and kIV (complex) karsts.

  9. High-resolution Urban Image Classification Using Extended Features

    SciTech Connect

    Vatsavai, Raju

    2011-01-01

    High-resolution image classification poses several challenges because the typical object size is much larger than the pixel resolution. Any given pixel (spectral features at that location) by itself is not a good indicator of the object it belongs to without looking at the broader spatial footprint. Therefore most modern machine learning approaches that are based on per-pixel spectral features are not very effective in high- resolution urban image classification. One way to overcome this problem is to extract features that exploit spatial contextual information. In this study, we evaluated several features in- cluding edge density, texture, and morphology. Several machine learning schemes were tested on the features extracted from a very high-resolution remote sensing image and results were presented.

  10. Featured Image: Star Clusters in M51

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2016-06-01

    This beautiful mosaic of images of the Whirlpool galaxy (M51) and its companion was taken with the Advanced Camera for Surveys on the Hubble Space Telescope. This nearby, grand-design spiral galaxy has a rich population of star clusters, making it both a stunning target for imagery and an excellent resource for learning about stellar formation and evolution. In a recent study, Rupali Chandar (University of Toledo) and collaborators cataloged over 3,800 compact star clusters within this galaxy. They then used this catalog to determine the distributions for the clusters ages, masses, and sizes, which can provide important clues as to how star clusters form, evolve, and are eventually disrupted. You can read more about their study and what they discovered in the paper below.CitationRupali Chandar et al 2016 ApJ 824 71. doi:10.3847/0004-637X/824/2/71

  11. Breast image feature learning with adaptive deconvolutional networks

    NASA Astrophysics Data System (ADS)

    Jamieson, Andrew R.; Drukker, Karen; Giger, Maryellen L.

    2012-03-01

    Feature extraction is a critical component of medical image analysis. Many computer-aided diagnosis approaches employ hand-designed, heuristic lesion extracted features. An alternative approach is to learn features directly from images. In this preliminary study, we explored the use of Adaptive Deconvolutional Networks (ADN) for learning high-level features in diagnostic breast mass lesion images with potential application to computer-aided diagnosis (CADx) and content-based image retrieval (CBIR). ADNs (Zeiler, et. al., 2011), are recently-proposed unsupervised, generative hierarchical models that decompose images via convolution sparse coding and max pooling. We trained the ADNs to learn multiple layers of representation for two breast image data sets on two different modalities (739 full field digital mammography (FFDM) and 2393 ultrasound images). Feature map calculations were accelerated by use of GPUs. Following Zeiler et. al., we applied the Spatial Pyramid Matching (SPM) kernel (Lazebnik, et. al., 2006) on the inferred feature maps and combined this with a linear support vector machine (SVM) classifier for the task of binary classification between cancer and non-cancer breast mass lesions. Non-linear, local structure preserving dimension reduction, Elastic Embedding (Carreira-Perpiñán, 2010), was then used to visualize the SPM kernel output in 2D and qualitatively inspect image relationships learned. Performance was found to be competitive with current CADx schemes that use human-designed features, e.g., achieving a 0.632+ bootstrap AUC (by case) of 0.83 [0.78, 0.89] for an ultrasound image set (1125 cases).

  12. Morphological Feature Extraction for Automatic Registration of Multispectral Images

    NASA Technical Reports Server (NTRS)

    Plaza, Antonio; LeMoigne, Jacqueline; Netanyahu, Nathan S.

    2007-01-01

    The task of image registration can be divided into two major components, i.e., the extraction of control points or features from images, and the search among the extracted features for the matching pairs that represent the same feature in the images to be matched. Manual extraction of control features can be subjective and extremely time consuming, and often results in few usable points. On the other hand, automated feature extraction allows using invariant target features such as edges, corners, and line intersections as relevant landmarks for registration purposes. In this paper, we present an extension of a recently developed morphological approach for automatic extraction of landmark chips and corresponding windows in a fully unsupervised manner for the registration of multispectral images. Once a set of chip-window pairs is obtained, a (hierarchical) robust feature matching procedure, based on a multiresolution overcomplete wavelet decomposition scheme, is used for registration purposes. The proposed method is validated on a pair of remotely sensed scenes acquired by the Advanced Land Imager (ALI) multispectral instrument and the Hyperion hyperspectral instrument aboard NASA's Earth Observing-1 satellite.

  13. Featured Image: Mapping Jupiter with Hubble

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2016-07-01

    Zonal wind profile for Jupiter, describing the speed and direction of its winds at each latitude. [Simon et al. 2015]This global map of Jupiters surface (click for the full view!) was generated by the Hubble Outer Planet Atmospheres Legacy (OPAL) program, which aims to createnew yearly global maps for each of the outer planets. Presented in a study led by Amy Simon (NASA Goddard Space Flight Center), the map above is the first generated for Jupiter in the first year of the OPAL campaign. It provides a detailed look at Jupiters atmospheric structure including the Great Red Spot and allowed the authors to measure the speed and direction of the wind across Jupiters latitudes, constructing an updated zonal wind profile for Jupiter.In contrast to this study, the Juno mission (which will be captured into Jupiters orbit today after a 5-year journey to Jupiter!) will be focusing more on the features below Jupiters surface, studying its deep atmosphere and winds. Some of Junos primary goals are to learn about Jupiters composition, gravitational field, magnetic field, and polar magnetosphere. You can follow along with the NASATV livestream as Juno arrives at Jupiter tonight; orbit insertion coverage starts at 10:30 EDT.CitationAmy A. Simon et al 2015 ApJ 812 55. doi:10.1088/0004-637X/812/1/55

  14. Learning Hierarchical Spectral-Spatial Features for Hyperspectral Image Classification.

    PubMed

    Zhou, Yicong; Wei, Yantao

    2016-07-01

    This paper proposes a spectral-spatial feature learning (SSFL) method to obtain robust features of hyperspectral images (HSIs). It combines the spectral feature learning and spatial feature learning in a hierarchical fashion. Stacking a set of SSFL units, a deep hierarchical model called the spectral-spatial networks (SSN) is further proposed for HSI classification. SSN can exploit both discriminative spectral and spatial information simultaneously. Specifically, SSN learns useful high-level features by alternating between spectral and spatial feature learning operations. Then, kernel-based extreme learning machine (KELM), a shallow neural network, is embedded in SSN to classify image pixels. Extensive experiments are performed on two benchmark HSI datasets to verify the effectiveness of SSN. Compared with state-of-the-art methods, SSN with a deep hierarchical architecture obtains higher classification accuracy in terms of the overall accuracy, average accuracy, and kappa ( κ ) coefficient of agreement, especially when the number of the training samples is small.

  15. Automated blood vessel extraction using local features on retinal images

    NASA Astrophysics Data System (ADS)

    Hatanaka, Yuji; Samo, Kazuki; Tajima, Mikiya; Ogohara, Kazunori; Muramatsu, Chisako; Okumura, Susumu; Fujita, Hiroshi

    2016-03-01

    An automated blood vessel extraction using high-order local autocorrelation (HLAC) on retinal images is presented. Although many blood vessel extraction methods based on contrast have been proposed, a technique based on the relation of neighbor pixels has not been published. HLAC features are shift-invariant; therefore, we applied HLAC features to retinal images. However, HLAC features are weak to turned image, thus a method was improved by the addition of HLAC features to a polar transformed image. The blood vessels were classified using an artificial neural network (ANN) with HLAC features using 105 mask patterns as input. To improve performance, the second ANN (ANN2) was constructed by using the green component of the color retinal image and the four output values of ANN, Gabor filter, double-ring filter and black-top-hat transformation. The retinal images used in this study were obtained from the "Digital Retinal Images for Vessel Extraction" (DRIVE) database. The ANN using HLAC output apparent white values in the blood vessel regions and could also extract blood vessels with low contrast. The outputs were evaluated using the area under the curve (AUC) based on receiver operating characteristics (ROC) analysis. The AUC of ANN2 was 0.960 as a result of our study. The result can be used for the quantitative analysis of the blood vessels.

  16. Fingerprint image segmentation based on multi-features histogram analysis

    NASA Astrophysics Data System (ADS)

    Wang, Peng; Zhang, Youguang

    2007-11-01

    An effective fingerprint image segmentation based on multi-features histogram analysis is presented. We extract a new feature, together with three other features to segment fingerprints. Two of these four features, each of which is related to one of the other two, are reciprocals with each other, so features are divided into two groups. These two features' histograms are calculated respectively to determine which feature group is introduced to segment the aim-fingerprint. The features could also divide fingerprints into two classes with high and low quality. Experimental results show that our algorithm could classify foreground and background effectively with lower computational cost, and it can also reduce pseudo-minutiae detected and improve the performance of AFIS.

  17. Featured Image: A Looping Stellar Stream

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2016-11-01

    This negative image of NGC 5907 (originally published inMartinez-Delgadoet al. 2008; click for the full view!) reveals the faint stellar stream that encircles the galaxy, forming loops around it a fossil of a recent merger. Mergers between galaxies come in several different flavors: major mergers, in which the merging galaxies are within a 1:5 ratio in stellar mass; satellite cannibalism, in which a large galaxy destroys a small satellite less than a 50th of its size; and the in-between case of minor mergers, in which the merging galaxieshave stellar mass ratios between 1:5 and 1:50. These minor mergers are thought to be relatively common, and they can have a significant effect on the dynamics and structure of the primary galaxy. A team of scientists led by Seppo Laine (Spitzer Science Center Caltech) has recently analyzed the metallicity and age of the stellar population in the stream around NGC 5907. By fitting these observations with a stellar population synthesis model, they conclude that this stream is an example of a massive minor merger, with a stellar mass ratio of at least 1:8. For more information, check out the paper below!CitationSeppo Laine et al 2016 AJ 152 72. doi:10.3847/0004-6256/152/3/72

  18. Featured Image: Orbiting Stars Share an Envelope

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2016-03-01

    This beautiful series of snapshots from a simulation (click for a better look!) shows what happens when two stars in a binary system become enclosed in the same stellar envelope. In this binary system, one of the stars has exhausted its hydrogen fuel and become a red giant, complete with an expanding stellar envelope composed of hydrogen and helium. Eventually, the envelope expands so much that the companion star falls into it, where it releases gravitational potential energy into the common envelope. A team led by Sebastian Ohlmann (Heidelberg Institute for Theoretical Studies and University of Wrzburg) recently performed hydrodynamic simulations of this process. Ohlmann and collaborators discovered that the energy release eventually triggers large-scale flow instabilities, which leads to turbulence within the envelope. This process has important consequences for how these systems next evolve (for instance, determining whether or not a supernova occurs!). You can check out the authors video of their simulated stellar inspiral below, or see their paper for more images and results from their study.CitationSebastian T. Ohlmann et al 2016 ApJ 816 L9. doi:10.3847/2041-8205/816/1/L9

  19. Featured Image: Tests of an MHD Code

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2016-09-01

    Creating the codes that are used to numerically model astrophysical systems takes a lot of work and a lot of testing! A new, publicly available moving-mesh magnetohydrodynamics (MHD) code, DISCO, is designed to model 2D and 3D orbital fluid motion, such as that of astrophysical disks. In a recent article, DISCO creator Paul Duffell (University of California, Berkeley) presents the code and the outcomes from a series of standard tests of DISCOs stability, accuracy, and scalability.From left to right and top to bottom, the test outputs shown above are: a cylindrical Kelvin-Helmholtz flow (showing off DISCOs numerical grid in 2D), a passive scalar in a smooth vortex (can DISCO maintain contact discontinuities?), a global look at the cylindrical Kelvin-Helmholtz flow, a Jupiter-mass planet opening a gap in a viscous disk, an MHD flywheel (a test of DISCOs stability), an MHD explosion revealing shock structures, an MHD rotor (a more challenging version of the explosion), a Flock 3D MRI test (can DISCO study linear growth of the magnetorotational instability in disks?), and a nonlinear 3D MRI test.Check out the gif below for a closer look at each of these images, or follow the link to the original article to see even more!CitationPaul C. Duffell 2016 ApJS 226 2. doi:10.3847/0067-0049/226/1/2

  20. Featured Image: A Gap in TW Hydrae

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2016-04-01

    This remarkable image (click for the full view!) is a high-resolution map of the 870 m light emitted by the protoplanetary disk surrounding the young solar analog TW Hydrae. A recent study led by Sean Andrews (Harvard-Smithsonian Center for Astrophysics) presents these observations, obtained with the long-baseline configuration of the Atacama Large Millimeter/submillimeter Array (ALMA) at an unprecedented spatial resolution of ~1 AU. The data represent the distribution of millimeter-sized dust grains in this disk, revealing a beautiful concentric ring structure out to a radial distance of 60 AU from the host star. The apparent gaps in the disk could have anyof three origins:Chemical: apparent gaps can becaused by condensation fronts of volatilesMagnetic: apparent gaps can becaused by radial magnetic pressure variationsDynamic: actual gaps can becaused by the clearing of dust by young planets.For more information, check out the paper below!CitationSean M. Andrews et al 2016 ApJ 820 L40. doi:10.3847/2041-8205/820/2/L40

  1. Featured Image: Fireball After a Temporary Capture?

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2016-06-01

    This image of a fireball was captured in the Czech Republic by cameras at a digital autonomous observatory in the village of Kunak. This observatory is part of a network of stations known as the European Fireball Network, and this particular meteoroid detection, labeled EN130114, is notable because it has the lowest initial velocity of any natural object ever observed by the network. Led by David Clark (University of Western Ontario), the authors of a recent study speculate that before this meteoroid impacted Earth, it may have been a Temporarily Captured Orbiter (TCO). TCOs are near-Earth objects that make a few orbits of Earth before returning to heliocentric orbits. Only one has ever been observed to date, and though they are thought to make up 0.1% of all meteoroids, EN130114 is the first event ever detected that exhibits conclusive behavior of a TCO. For more information on EN130114 and why TCOs are important to study, check out the paper below!CitationDavid L. Clark et al 2016 AJ 151 135. doi:10.3847/0004-6256/151/6/135

  2. Research of image matching algorithm based on local features

    NASA Astrophysics Data System (ADS)

    Sun, Wei

    2015-07-01

    For the problem of low efficiency in SIFT algorithm while using exhaustive method to search the nearest neighbor and next nearest neighbor of feature points, this paper introduces K-D tree algorithm, to index the feature points extracted in database images according to the tree structure, at the same time, using the concept of a weighted priority, further improves the algorithm, to further enhance the efficiency of feature matching.

  3. Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion

    PubMed Central

    Zhao, Yuanshen; Gong, Liang; Huang, Yixiang; Liu, Chengliang

    2016-01-01

    Automatic recognition of mature fruits in a complex agricultural environment is still a challenge for an autonomous harvesting robot due to various disturbances existing in the background of the image. The bottleneck to robust fruit recognition is reducing influence from two main disturbances: illumination and overlapping. In order to recognize the tomato in the tree canopy using a low-cost camera, a robust tomato recognition algorithm based on multiple feature images and image fusion was studied in this paper. Firstly, two novel feature images, the  a*-component image and the I-component image, were extracted from the L*a*b* color space and luminance, in-phase, quadrature-phase (YIQ) color space, respectively. Secondly, wavelet transformation was adopted to fuse the two feature images at the pixel level, which combined the feature information of the two source images. Thirdly, in order to segment the target tomato from the background, an adaptive threshold algorithm was used to get the optimal threshold. The final segmentation result was processed by morphology operation to reduce a small amount of noise. In the detection tests, 93% target tomatoes were recognized out of 200 overall samples. It indicates that the proposed tomato recognition method is available for robotic tomato harvesting in the uncontrolled environment with low cost. PMID:26840313

  4. Feature matching method for uncorrected fisheye lens image

    NASA Astrophysics Data System (ADS)

    Liu, Na; Zhang, Baofeng; Jiao, Yingkui; Zhu, Junchao

    2016-01-01

    Traditional matching algorithms cannot be directly applied to the fisheye image matching for large distortion existing in fisheye image. Therefore, a matching algorithm based on uncorrected fisheye images is proposed. This algorithm adopts a local feature description method which combines MSER detector with CSLBP descriptor to obtain the image feature. First, the two uncorrected fisheye images captured by binocular vision system are described by the principle of epipolar constraint. Then the region detection is done with MSER and the ellipse fitting is used to the obtained regions. The MSER regions are described by CSLBP subsequently. Finally, in order to exclude the mismatching points of initial match, random sample consensus (RANSAC) algorithm has been adopted to achieve exact match. Experiments show that the method has a good effect on the uncorrected fisheye image matching.

  5. Invariant Feature Matching for Image Registration Application Based on New Dissimilarity of Spatial Features.

    PubMed

    Mousavi Kahaki, Seyed Mostafa; Nordin, Md Jan; Ashtari, Amir H; J Zahra, Sophia

    2016-01-01

    An invariant feature matching method is proposed as a spatially invariant feature matching approach. Deformation effects, such as affine and homography, change the local information within the image and can result in ambiguous local information pertaining to image points. New method based on dissimilarity values, which measures the dissimilarity of the features through the path based on Eigenvector properties, is proposed. Evidence shows that existing matching techniques using similarity metrics--such as normalized cross-correlation, squared sum of intensity differences and correlation coefficient--are insufficient for achieving adequate results under different image deformations. Thus, new descriptor's similarity metrics based on normalized Eigenvector correlation and signal directional differences, which are robust under local variation of the image information, are proposed to establish an efficient feature matching technique. The method proposed in this study measures the dissimilarity in the signal frequency along the path between two features. Moreover, these dissimilarity values are accumulated in a 2D dissimilarity space, allowing accurate corresponding features to be extracted based on the cumulative space using a voting strategy. This method can be used in image registration applications, as it overcomes the limitations of the existing approaches. The output results demonstrate that the proposed technique outperforms the other methods when evaluated using a standard dataset, in terms of precision-recall and corner correspondence. PMID:26985996

  6. Invariant Feature Matching for Image Registration Application Based on New Dissimilarity of Spatial Features

    PubMed Central

    Mousavi Kahaki, Seyed Mostafa; Nordin, Md Jan; Ashtari, Amir H.; J. Zahra, Sophia

    2016-01-01

    An invariant feature matching method is proposed as a spatially invariant feature matching approach. Deformation effects, such as affine and homography, change the local information within the image and can result in ambiguous local information pertaining to image points. New method based on dissimilarity values, which measures the dissimilarity of the features through the path based on Eigenvector properties, is proposed. Evidence shows that existing matching techniques using similarity metrics—such as normalized cross-correlation, squared sum of intensity differences and correlation coefficient—are insufficient for achieving adequate results under different image deformations. Thus, new descriptor’s similarity metrics based on normalized Eigenvector correlation and signal directional differences, which are robust under local variation of the image information, are proposed to establish an efficient feature matching technique. The method proposed in this study measures the dissimilarity in the signal frequency along the path between two features. Moreover, these dissimilarity values are accumulated in a 2D dissimilarity space, allowing accurate corresponding features to be extracted based on the cumulative space using a voting strategy. This method can be used in image registration applications, as it overcomes the limitations of the existing approaches. The output results demonstrate that the proposed technique outperforms the other methods when evaluated using a standard dataset, in terms of precision-recall and corner correspondence. PMID:26985996

  7. Imaging features of Burkitt lymphoma in pediatric patients

    PubMed Central

    Derinkuyu, Betül Emine; Boyunağa, Öznur; Öztunalı, Çiğdem; Tekkeşin, Funda; Damar, Çağrı; Alımlı, Ayşe Gül; Okur, Arzu

    2016-01-01

    Burkitt lymphoma is an aggressive and rapidly growing tumor that is curable and highly sensitive to chemotherapy. It can affect almost every tissue in the body, producing various clinical presentations and imaging appearances, according to the predilection of the different subtypes for certain sites. Awareness of its diagnostically specific imaging appearances plays an important role in rapid detection and treatment. In this pictorial review, we aimed to identify the most common imaging features of Burkitt lymphoma in pediatric patients. PMID:26611257

  8. Image Processing Techniques and Feature Recognition in Solar Physics

    NASA Astrophysics Data System (ADS)

    Aschwanden, Markus J.

    2010-04-01

    This review presents a comprehensive and systematic overview of image-processing techniques that are used in automated feature-detection algorithms applied to solar data: i) image pre-processing procedures, ii) automated detection of spatial features, iii) automated detection and tracking of temporal features (events), and iv) post-processing tasks, such as visualization of solar imagery, cataloguing, statistics, theoretical modeling, prediction, and forecasting. For each aspect the most recent developments and science results are highlighted. We conclude with an outlook on future trends.

  9. Automated feature extraction and classification from image sources

    USGS Publications Warehouse

    U.S. Geological Survey

    1995-01-01

    The U.S. Department of the Interior, U.S. Geological Survey (USGS), and Unisys Corporation have completed a cooperative research and development agreement (CRADA) to explore automated feature extraction and classification from image sources. The CRADA helped the USGS define the spectral and spatial resolution characteristics of airborne and satellite imaging sensors necessary to meet base cartographic and land use and land cover feature classification requirements and help develop future automated geographic and cartographic data production capabilities. The USGS is seeking a new commercial partner to continue automated feature extraction and classification research and development.

  10. Detection of classical 17p11.2 deletions, an atypical deletion and RAI1 alterations in patients with features suggestive of Smith–Magenis syndrome

    PubMed Central

    Vieira, Gustavo H; Rodriguez, Jayson D; Carmona-Mora, Paulina; Cao, Lei; Gamba, Bruno F; Carvalho, Daniel R; de Rezende Duarte, Andréa; Santos, Suely R; de Souza, Deise H; DuPont, Barbara R; Walz, Katherina; Moretti-Ferreira, Danilo; Srivastava, Anand K

    2012-01-01

    Smith–Magenis syndrome (SMS) is a complex disorder whose clinical features include mild to severe intellectual disability with speech delay, growth failure, brachycephaly, flat midface, short broad hands, and behavioral problems. SMS is typically caused by a large deletion on 17p11.2 that encompasses multiple genes including the retinoic acid induced 1, RAI1, gene or a mutation in the RAI1 gene. Here we have evaluated 30 patients with suspected SMS and identified SMS-associated classical 17p11.2 deletions in six patients, an atypical deletion of ∼139 kb that partially deletes the RAI1 gene in one patient, and RAI1 gene nonsynonymous alterations of unknown significance in two unrelated patients. The RAI1 mutant proteins showed no significant alterations in molecular weight, subcellular localization and transcriptional activity. Clinical features of patients with or without 17p11.2 deletions and mutations involving the RAI1 gene were compared to identify phenotypes that may be useful in diagnosing patients with SMS. PMID:21897445

  11. Atypical Teratomas of the Pineal

    PubMed Central

    Lewis, I.; Baxter, D. W.; Stratford, J. G.

    1963-01-01

    Atypical teratomas of the pineal were studied pathologically and clinically, and five illustrative cases are described. The results of three postmortem examinations are available, while two of the patients are living, one leading a normal life. Pathological verification revealed that two had suprasellar “ectopic” pinealomas. One neoplasm was located in the pineal (collicular) region. The histology of the tumours was identical, consisting of small cells resembling lymphocytes and large cells with prominent nucleoli and mitoses. This feature plus the midline location led to adoption of the term “atypical teratoma”. Patients with collicular pinealomas presented with headache, vomiting, papilledema, Parinaud's syndrome and, rarely, nystagmus retractorius. Diabetes insipidus, visual difficulty and hypopituitarism were characteristic features in those with suprasellar neoplasms. Treatment of collicular pinealoma has consisted of the use of a palliative shunt followed by a course of radiation. Chiasmal decompression and radiation have produced favourable results in patients with suprasellar pinealoma. ImagesFig. 1Fig. 2Fig. 3Fig. 4Fig. 5Fig. 6Fig. 7Fig. 8Fig. 9Fig. 10Fig. 11Fig. 12 PMID:20327617

  12. A novel feature point matching method of remote sensing images

    NASA Astrophysics Data System (ADS)

    Xu, Yuanquan; Wang, Han; Zhang, Xubing; Wang, ShaoJun

    2015-12-01

    The method of feature-based registration has been successful applied in registration of multi-source remote sensing images. Unfortunately, the mismatching still exists due to the complex textures, spectrum variation, nonlinear distortion and the large scale change. In this paper, we proposed a novel feature point matching method of multi-source remote sensing images. Firstly, the Fast-Hessian detector is to extract the feature points which are described by the SURF descriptor in the following step. After that, we analyze the local neighborhood structures of the feature points, and formulate point matching as an optimization problem to preserve local neighborhood structures. The shape context distances of the feature points are utilized to initialize matching probability matrix. Then relaxation labeling is adopted to update the probability matrix and refine the matching, which is aimed to maximize the value of the object function deduced based on preserving local neighborhood structures. Subsequently, the mismatching elimination method based on affine transformation and distance measurement is used to eliminate the residual mismatching points. During the abovementioned matching produce, the multi-resolution analysis method is adopted to decrease the scale difference between the multi-source remote sensing images. Also the mutual information method is utilized to match the feature points of the down sampling and the original images. The experimental results are shown that the proposed method was robust and efficient for registration of multi-source remote sensing images.

  13. Integration of Image-Derived and Pos-Derived Features for Image Blur Detection

    NASA Astrophysics Data System (ADS)

    Teo, Tee-Ann; Zhan, Kai-Zhi

    2016-06-01

    The image quality plays an important role for Unmanned Aerial Vehicle (UAV)'s applications. The small fixed wings UAV is suffering from the image blur due to the crosswind and the turbulence. Position and Orientation System (POS), which provides the position and orientation information, is installed onto an UAV to enable acquisition of UAV trajectory. It can be used to calculate the positional and angular velocities when the camera shutter is open. This study proposes a POS-assisted method to detect the blur image. The major steps include feature extraction, blur image detection and verification. In feature extraction, this study extracts different features from images and POS. The image-derived features include mean and standard deviation of image gradient. For POS-derived features, we modify the traditional degree-of-linear-blur (blinear) method to degree-of-motion-blur (bmotion) based on the collinear condition equations and POS parameters. Besides, POS parameters such as positional and angular velocities are also adopted as POS-derived features. In blur detection, this study uses Support Vector Machines (SVM) classifier and extracted features (i.e. image information, POS data, blinear and bmotion) to separate blur and sharp UAV images. The experiment utilizes SenseFly eBee UAV system. The number of image is 129. In blur image detection, we use the proposed degree-of-motion-blur and other image features to classify the blur image and sharp images. The classification result shows that the overall accuracy using image features is only 56%. The integration of image-derived and POS-derived features have improved the overall accuracy from 56% to 76% in blur detection. Besides, this study indicates that the performance of the proposed degree-of-motion-blur is better than the traditional degree-of-linear-blur.

  14. A high performance parallel computing architecture for robust image features

    NASA Astrophysics Data System (ADS)

    Zhou, Renyan; Liu, Leibo; Wei, Shaojun

    2014-03-01

    A design of parallel architecture for image feature detection and description is proposed in this article. The major component of this architecture is a 2D cellular network composed of simple reprogrammable processors, enabling the Hessian Blob Detector and Haar Response Calculation, which are the most computing-intensive stage of the Speeded Up Robust Features (SURF) algorithm. Combining this 2D cellular network and dedicated hardware for SURF descriptors, this architecture achieves real-time image feature detection with minimal software in the host processor. A prototype FPGA implementation of the proposed architecture achieves 1318.9 GOPS general pixel processing @ 100 MHz clock and achieves up to 118 fps in VGA (640 × 480) image feature detection. The proposed architecture is stand-alone and scalable so it is easy to be migrated into VLSI implementation.

  15. Direct extraction of topographic features from gray scale haracter images

    SciTech Connect

    Seong-Whan Lee; Young Joon Kim

    1994-12-31

    Optical character recognition (OCR) traditionally applies to binary-valued imagery although text is always scanned and stored in gray scale. However, binarization of multivalued image may remove important topological information from characters and introduce noise to character background. In order to avoid this problem, it is indispensable to develop a method which can minimize the information loss due to binarization by extracting features directly from gray scale character images. In this paper, we propose a new method for the direct extraction of topographic features from gray scale character images. By comparing the proposed method with the Wang and Pavlidis`s method we realized that the proposed method enhanced the performance of topographic feature extraction by computing the directions of principal curvature efficiently and prevented the extraction of unnecessary features. We also show that the proposed method is very effective for gray scale skeletonization compared to Levi and Montanari`s method.

  16. Automated Image Registration Using Morphological Region of Interest Feature Extraction

    NASA Technical Reports Server (NTRS)

    Plaza, Antonio; LeMoigne, Jacqueline; Netanyahu, Nathan S.

    2005-01-01

    With the recent explosion in the amount of remotely sensed imagery and the corresponding interest in temporal change detection and modeling, image registration has become increasingly important as a necessary first step in the integration of multi-temporal and multi-sensor data for applications such as the analysis of seasonal and annual global climate changes, as well as land use/cover changes. The task of image registration can be divided into two major components: (1) the extraction of control points or features from images; and (2) the search among the extracted features for the matching pairs that represent the same feature in the images to be matched. Manual control feature extraction can be subjective and extremely time consuming, and often results in few usable points. Automated feature extraction is a solution to this problem, where desired target features are invariant, and represent evenly distributed landmarks such as edges, corners and line intersections. In this paper, we develop a novel automated registration approach based on the following steps. First, a mathematical morphology (MM)-based method is used to obtain a scale-orientation morphological profile at each image pixel. Next, a spectral dissimilarity metric such as the spectral information divergence is applied for automated extraction of landmark chips, followed by an initial approximate matching. This initial condition is then refined using a hierarchical robust feature matching (RFM) procedure. Experimental results reveal that the proposed registration technique offers a robust solution in the presence of seasonal changes and other interfering factors. Keywords-Automated image registration, multi-temporal imagery, mathematical morphology, robust feature matching.

  17. Histopathological Image Classification Using Discriminative Feature-Oriented Dictionary Learning.

    PubMed

    Vu, Tiep Huu; Mousavi, Hojjat Seyed; Monga, Vishal; Rao, Ganesh; Rao, U K Arvind

    2016-03-01

    In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structures. In this paper, we propose an automatic feature discovery framework via learning class-specific dictionaries and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific dictionaries such that under a sparsity constraint, the learned dictionaries allow representing a new image sample parsimoniously via the dictionary corresponding to the class identity of the sample. At the same time, the dictionary is designed to be poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian kidney, lung and spleen images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, reveal the merits of our proposal over state-of-the-art alternatives. Moreover, we demonstrate that DFDL exhibits a more graceful decay in classification accuracy against the number of training images which is highly desirable in practice where generous training is often not available.

  18. Categorizing biomedicine images using novel image features and sparse coding representation

    PubMed Central

    2013-01-01

    Background Images embedded in biomedical publications carry rich information that often concisely summarize key hypotheses adopted, methods employed, or results obtained in a published study. Therefore, they offer valuable clues for understanding main content in a biomedical publication. Prior studies have pointed out the potential of mining images embedded in biomedical publications for automatically understanding and retrieving such images' associated source documents. Within the broad area of biomedical image processing, categorizing biomedical images is a fundamental step for building many advanced image analysis, retrieval, and mining applications. Similar to any automatic categorization effort, discriminative image features can provide the most crucial aid in the process. Method We observe that many images embedded in biomedical publications carry versatile annotation text. Based on the locations of and the spatial relationships between these text elements in an image, we thus propose some novel image features for image categorization purpose, which quantitatively characterize the spatial positions and distributions of text elements inside a biomedical image. We further adopt a sparse coding representation (SCR) based technique to categorize images embedded in biomedical publications by leveraging our newly proposed image features. Results we randomly selected 990 images of the JPG format for use in our experiments where 310 images were used as training samples and the rest were used as the testing cases. We first segmented 310 sample images following the our proposed procedure. This step produced a total of 1035 sub-images. We then manually labeled all these sub-images according to the two-level hierarchical image taxonomy proposed by [1]. Among our annotation results, 316 are microscopy images, 126 are gel electrophoresis images, 135 are line charts, 156 are bar charts, 52 are spot charts, 25 are tables, 70 are flow charts, and the remaining 155 images are

  19. Effective feature selection for image steganalysis using extreme learning machine

    NASA Astrophysics Data System (ADS)

    Feng, Guorui; Zhang, Haiyan; Zhang, Xinpeng

    2014-11-01

    Image steganography delivers secret data by slight modifications of the cover. To detect these data, steganalysis tries to create some features to embody the discrepancy between the cover and steganographic images. Therefore, the urgent problem is how to design an effective classification architecture for given feature vectors extracted from the images. We propose an approach to automatically select effective features based on the well-known JPEG steganographic methods. This approach, referred to as extreme learning machine revisited feature selection (ELM-RFS), can tune input weights in terms of the importance of input features. This idea is derived from cross-validation learning and one-dimensional (1-D) search. While updating input weights, we seek the energy decreasing direction using the leave-one-out (LOO) selection. Furthermore, we optimize the 1-D energy function instead of directly discarding the least significant feature. Since recent Liu features can gain considerable low detection errors compared to a previous JPEG steganalysis, the experimental results demonstrate that the new approach results in less classification error than other classifiers such as SVM, Kodovsky ensemble classifier, direct ELM-LOO learning, kernel ELM, and conventional ELM in Liu features. Furthermore, ELM-RFS achieves a similar performance with a deep Boltzmann machine using less training time.

  20. NSCLC tumor shrinkage prediction using quantitative image features.

    PubMed

    Hunter, Luke A; Chen, Yi Pei; Zhang, Lifei; Matney, Jason E; Choi, Haesun; Kry, Stephen F; Martel, Mary K; Stingo, Francesco; Liao, Zhongxing; Gomez, Daniel; Yang, Jinzhong; Court, Laurence E

    2016-04-01

    The objective of this study was to develop a quantitative image feature model to predict non-small cell lung cancer (NSCLC) volume shrinkage from pre-treatment CT images. 64 stage II-IIIB NSCLC patients with similar treatments were all imaged using the same CT scanner and protocol. For each patient, the planning gross tumor volume (GTV) was deformed onto the week 6 treatment image, and tumor shrinkage was quantified as the deformed GTV volume divided by the planning GTV volume. Geometric, intensity histogram, absolute gradient image, co-occurrence matrix, and run-length matrix image features were extracted from each planning GTV. Prediction models were generated using principal component regression with simulated annealing subset selection. Performance was quantified using the mean squared error (MSE) between the predicted and observed tumor shrinkages. Permutation tests were used to validate the results. The optimal prediction model gave a strong correlation between the observed and predicted tumor shrinkages with r=0.81 and MSE=8.60×10(-3). Compared to predictions based on the mean population shrinkage this resulted in a 2.92 fold reduction in MSE. In conclusion, this study indicated that quantitative image features extracted from existing pre-treatment CT images can successfully predict tumor shrinkage and provide additional information for clinical decisions regarding patient risk stratification, treatment, and prognosis. PMID:26878137

  1. Feature maps driven no-reference image quality prediction of authentically distorted images

    NASA Astrophysics Data System (ADS)

    Ghadiyaram, Deepti; Bovik, Alan C.

    2015-03-01

    Current blind image quality prediction models rely on benchmark databases comprised of singly and synthetically distorted images, thereby learning image features that are only adequate to predict human perceived visual quality on such inauthentic distortions. However, real world images often contain complex mixtures of multiple distortions. Rather than a) discounting the effect of these mixtures of distortions on an image's perceptual quality and considering only the dominant distortion or b) using features that are only proven to be efficient for singly distorted images, we deeply study the natural scene statistics of authentically distorted images, in different color spaces and transform domains. We propose a feature-maps-driven statistical approach which avoids any latent assumptions about the type of distortion(s) contained in an image, and focuses instead on modeling the remarkable consistencies in the scene statistics of real world images in the absence of distortions. We design a deep belief network that takes model-based statistical image features derived from a very large database of authentically distorted images as input and discovers good feature representations by generalizing over different distortion types, mixtures, and severities, which are later used to learn a regressor for quality prediction. We demonstrate the remarkable competence of our features for improving automatic perceptual quality prediction on a benchmark database and on the newly designed LIVE Authentic Image Quality Challenge Database and show that our approach of combining robust statistical features and the deep belief network dramatically outperforms the state-of-the-art.

  2. Stress fractures: pathophysiology, clinical presentation, imaging features, and treatment options.

    PubMed

    Matcuk, George R; Mahanty, Scott R; Skalski, Matthew R; Patel, Dakshesh B; White, Eric A; Gottsegen, Christopher J

    2016-08-01

    Stress fracture, in its most inclusive description, includes both fatigue and insufficiency fracture. Fatigue fractures, sometimes equated with the term "stress fractures," are most common in runners and other athletes and typically occur in the lower extremities. These fractures are the result of abnormal, cyclical loading on normal bone leading to local cortical resorption and fracture. Insufficiency fractures are common in elderly populations, secondary to osteoporosis, and are typically located in and around the pelvis. They are a result of normal or traumatic loading on abnormal bone. Subchondral insufficiency fractures of the hip or knee may cause acute pain that may present in the emergency setting. Medial tibial stress syndrome is a type of stress injury of the tibia related to activity and is a clinical syndrome encompassing a range of injuries from stress edema to frank-displaced fracture. Atypical subtrochanteric femoral fracture associated with long-term bisphosphonate therapy is also a recently discovered entity that needs early recognition to prevent progression to a complete fracture. Imaging recommendations for evaluation of stress fractures include initial plain radiographs followed, if necessary, by magnetic resonance imaging (MRI), which is preferred over computed tomography (CT) and bone scintigraphy. Radiographs are the first-line modality and may reveal linear sclerosis and periosteal reaction prior to the development of a frank fracture. MRI is highly sensitive with findings ranging from periosteal edema to bone marrow and intracortical signal abnormality. Additionally, a brief description of relevant clinical management of stress fractures is included.

  3. MR imaging evaluation of perianal fistulas: spectrum of imaging features.

    PubMed

    de Miguel Criado, Jaime; del Salto, Laura García; Rivas, Patricia Fraga; del Hoyo, Luis Felipe Aguilera; Velasco, Leticia Gutiérrez; de las Vacas, M Isabel Díez Pérez; Marco Sanz, Ana G; Paradela, Marcos Manzano; Moreno, Eduardo Fraile

    2012-01-01

    Perianal fistulization is an inflammatory condition that affects the region around the anal canal, causing significant morbidity and often requiring repeated surgical treatments due to its high tendency to recur. To adopt the best surgical strategy and avoid recurrences, it is necessary to obtain precise radiologic information about the location of the fistulous track and the affected pelvic structures. Until recently, imaging techniques played a limited role in evaluation of perianal fistulas. However, magnetic resonance (MR) imaging now provides more precise information on the anatomy of the anal canal, the anal sphincter complex, and the relationships of the fistula to the pelvic floor structures and the plane of the levator ani muscle. MR imaging allows precise definition of the fistulous track and identification of secondary fistulas or abscesses. It provides accurate information for appropriate surgical treatment, decreasing the incidence of recurrence and allowing side effects such as fecal incontinence to be avoided. Radiologists should be familiar with the anatomic and pathologic findings of perianal fistulas and classify them using the St James's University Hospital MR imaging-based grading system.

  4. Preliminary investigation into sources of uncertainty in quantitative imaging features.

    PubMed

    Fave, Xenia; Cook, Molly; Frederick, Amy; Zhang, Lifei; Yang, Jinzhong; Fried, David; Stingo, Francesco; Court, Laurence

    2015-09-01

    Several recent studies have demonstrated the potential for quantitative imaging features to classify non-small cell lung cancer (NSCLC) patients as high or low risk. However applying the results from one institution to another has been difficult because of the variations in imaging techniques and feature measurement. Our study was designed to determine the effect of some of these sources of uncertainty on image features extracted from computed tomography (CT) images of non-small cell lung cancer (NSCLC) tumors. CT images from 20 NSCLC patients were obtained for investigating the impact of four sources of uncertainty: Two region of interest (ROI) selection conditions (breathing phase and single-slice vs. whole volume) and two imaging protocol parameters (peak tube voltage and current). Texture values did not vary substantially with the choice of breathing phase; however, almost half (12 out of 28) of the measured textures did change significantly when measured from the average images compared to the end-of-exhale phase. Of the 28 features, 8 showed a significant variation when measured from the largest cross sectional slice compared to the entire tumor, but 14 were correlated to the entire tumor value. While simulating a decrease in tube voltage had a negligible impact on texture features, simulating a decrease in mA resulted in significant changes for 13 of the 23 texture values. Our results suggest that substantial variation exists when textures are measured under different conditions, and thus the development of a texture analysis standard would be beneficial for comparing features between patients and institutions. PMID:26004695

  5. Preliminary investigation into sources of uncertainty in quantitative imaging features.

    PubMed

    Fave, Xenia; Cook, Molly; Frederick, Amy; Zhang, Lifei; Yang, Jinzhong; Fried, David; Stingo, Francesco; Court, Laurence

    2015-09-01

    Several recent studies have demonstrated the potential for quantitative imaging features to classify non-small cell lung cancer (NSCLC) patients as high or low risk. However applying the results from one institution to another has been difficult because of the variations in imaging techniques and feature measurement. Our study was designed to determine the effect of some of these sources of uncertainty on image features extracted from computed tomography (CT) images of non-small cell lung cancer (NSCLC) tumors. CT images from 20 NSCLC patients were obtained for investigating the impact of four sources of uncertainty: Two region of interest (ROI) selection conditions (breathing phase and single-slice vs. whole volume) and two imaging protocol parameters (peak tube voltage and current). Texture values did not vary substantially with the choice of breathing phase; however, almost half (12 out of 28) of the measured textures did change significantly when measured from the average images compared to the end-of-exhale phase. Of the 28 features, 8 showed a significant variation when measured from the largest cross sectional slice compared to the entire tumor, but 14 were correlated to the entire tumor value. While simulating a decrease in tube voltage had a negligible impact on texture features, simulating a decrease in mA resulted in significant changes for 13 of the 23 texture values. Our results suggest that substantial variation exists when textures are measured under different conditions, and thus the development of a texture analysis standard would be beneficial for comparing features between patients and institutions.

  6. Simultenious binary hash and features learning for image retrieval

    NASA Astrophysics Data System (ADS)

    Frantc, V. A.; Makov, S. V.; Voronin, V. V.; Marchuk, V. I.; Semenishchev, E. A.; Egiazarian, K. O.; Agaian, S.

    2016-05-01

    Content-based image retrieval systems have plenty of applications in modern world. The most important one is the image search by query image or by semantic description. Approaches to this problem are employed in personal photo-collection management systems, web-scale image search engines, medical systems, etc. Automatic analysis of large unlabeled image datasets is virtually impossible without satisfactory image-retrieval technique. It's the main reason why this kind of automatic image processing has attracted so much attention during recent years. Despite rather huge progress in the field, semantically meaningful image retrieval still remains a challenging task. The main issue here is the demand to provide reliable results in short amount of time. This paper addresses the problem by novel technique for simultaneous learning of global image features and binary hash codes. Our approach provide mapping of pixel-based image representation to hash-value space simultaneously trying to save as much of semantic image content as possible. We use deep learning methodology to generate image description with properties of similarity preservation and statistical independence. The main advantage of our approach in contrast to existing is ability to fine-tune retrieval procedure for very specific application which allow us to provide better results in comparison to general techniques. Presented in the paper framework for data- dependent image hashing is based on use two different kinds of neural networks: convolutional neural networks for image description and autoencoder for feature to hash space mapping. Experimental results confirmed that our approach has shown promising results in compare to other state-of-the-art methods.

  7. Unsupervised Feature Learning for High-Resolution Satellite Image Classification

    SciTech Connect

    Cheriyadat, Anil M

    2013-01-01

    The rich data provided by high-resolution satellite imagery allow us to directly model geospatial neighborhoods by understanding their spatial and structural patterns. In this paper we explore an unsupervised feature learning approach to model geospatial neighborhoods for classification purposes. While pixel and object based classification approaches are widely used for satellite image analysis, often these approaches exploit the high-fidelity image data in a limited way. In this paper we extract low-level features to characterize the local neighborhood patterns. We exploit the unlabeled feature measurements in a novel way to learn a set of basis functions to derive new features. The derived sparse feature representation obtained by encoding the measured features in terms of the learned basis function set yields superior classification performance. We applied our technique on two challenging image datasets: ORNL dataset representing one-meter spatial resolution satellite imagery representing five land-use categories and, UCMERCED dataset consisting of 21 different categories representing sub-meter resolution overhead imagery. Our results are highly promising and, in the case of UCMERCED dataset we outperform the best results obtained for this dataset. We show that our feature extraction and learning methods are highly effective in developing a detection system that can be used to automatically scan large-scale high-resolution satellite imagery for detecting large-facility.

  8. Combinational feature optimization for classification of lung tissue images

    NASA Astrophysics Data System (ADS)

    Samala, Ravi K.; Zhukov, Tatyana; Zhang, Jianying; Tockman, Melvyn; Qian, Wei

    2010-03-01

    A novel approach to feature optimization for classification of lung carcinoma using tissue images is presented. The methodology uses a combination of three characteristics of computational features: F-measure, which is a representation of each feature towards classification, inter-correlation between features and pathology based information. The metadata provided from pathological parameters is used for mapping between computational features and biological information. Multiple regression analysis maps each category of features based on how pathology information is correlated with the size and location of cancer. Relatively the computational features represented the tumor size better than the location of the cancer. Based on the three criteria associated with the features, three sets of feature subsets with individual validation are evaluated to select the optimum feature subset. Based on the results from the three stages, the knowledgebase produces the best subset of features. An improvement of 5.5% was observed for normal Vs all abnormal cases with Az value of 0.731 and 74/114 correctly classified. The best Az value of 0.804 with 66/84 correct classification and improvement of 21.6% was observed for normal Vs adenocarcinoma.

  9. MRI and PET image fusion using fuzzy logic and image local features.

    PubMed

    Javed, Umer; Riaz, Muhammad Mohsin; Ghafoor, Abdul; Ali, Syed Sohaib; Cheema, Tanveer Ahmed

    2014-01-01

    An image fusion technique for magnetic resonance imaging (MRI) and positron emission tomography (PET) using local features and fuzzy logic is presented. The aim of proposed technique is to maximally combine useful information present in MRI and PET images. Image local features are extracted and combined with fuzzy logic to compute weights for each pixel. Simulation results show that the proposed scheme produces significantly better results compared to state-of-art schemes.

  10. Semi-supervised feature learning for hyperspectral image classification

    NASA Astrophysics Data System (ADS)

    Zhang, Pengfei; Cao, Liujuan; Wang, Cheng; Li, Jonathan

    2016-03-01

    Hyperspectral image has high-dimensional Spectral-spatial features, those features with some noisy and redundant information. Since redundant features can have significant adverse effect on learning performance. So efficient and robust feature selection methods are make the best of labeled and unlabeled points to extract meaningful features and eliminate noisy ones. On the other hand, obtaining sufficient accurate labeled data is either impossible or expensive. In order to take advantage of both precious labeled and unlabeled data points, in this paper, we propose a new semisupervised feature selection method, Firstly, we use labeled points are to enlarge the margin between data points from different classes; Secondly, we use unlabeled points to find the local structure of the data space; Finally, we compare our proposed algorithm with Fisher score, PCA and Laplacian score on HSI classification. Experimental results on benchmark hyperspectral data sets demonstrate the efficiency and effectiveness of our proposed algorithm.

  11. Feature-Based Digital Watermarking for Remote Sensing Images

    NASA Astrophysics Data System (ADS)

    Hsu, P.-H.; Chen, C.-C.

    2012-08-01

    With the rapid development of information and communication technology, people can acquire and distribute many kinds of digital data more conveniently than before. The consequence is that the "copyright protection" which prevents digital data from been duplicated illegally should be paid much more attention. Digital watermarking is the process of embedding visible or invisible information into a digital signal which may be used to verify its authenticity or the identity of its owners. In the past, digital watermarking technology has been successfully applied to the "copyright protection" of multimedia data, however the researches and applications of applying digital watermarking to geo-information data are still very inadequate. In this study, a novel digital watermarking algorithm based on the scale-space feature points is applied to the remote sensing images, and the robustness of the embedded digital watermark and the impact on satellite image quality are evaluated and analysed. This kind of feature points are commonly invariant to Image rotation, scaling and translation, therefore they naturally fit into the requirement of geometrically robust image watermarking. The experiment results show almost all extracted watermarks have high values of normal correlation and can be recognized clearly after the processing of image compression, brightness adjustment and contrast adjustment. In addition, most of the extracted watermarks are identified after the geometric attacks. Furthermore, the unsupervised image classification is implemented on the watermarked images to evaluate the image quality reduction and the results show that classification accuracy is affected slightly after embedding watermarks into the satellite images.

  12. Hdr Imaging for Feature Detection on Detailed Architectural Scenes

    NASA Astrophysics Data System (ADS)

    Kontogianni, G.; Stathopoulou, E. K.; Georgopoulos, A.; Doulamis, A.

    2015-02-01

    3D reconstruction relies on accurate detection, extraction, description and matching of image features. This is even truer for complex architectural scenes that pose needs for 3D models of high quality, without any loss of detail in geometry or color. Illumination conditions influence the radiometric quality of images, as standard sensors cannot depict properly a wide range of intensities in the same scene. Indeed, overexposed or underexposed pixels cause irreplaceable information loss and degrade digital representation. Images taken under extreme lighting environments may be thus prohibitive for feature detection/extraction and consequently for matching and 3D reconstruction. High Dynamic Range (HDR) images could be helpful for these operators because they broaden the limits of illumination range that Standard or Low Dynamic Range (SDR/LDR) images can capture and increase in this way the amount of details contained in the image. Experimental results of this study prove this assumption as they examine state of the art feature detectors applied both on standard dynamic range and HDR images.

  13. Atypical Clival Chordoma in an Adolescent without Imaging Evidence of Bone Involvement

    PubMed Central

    HASHIM, Hilwati; ROSMAN, Azmin Kass; ABDUL AZIZ, Aida; ROQIAH, Abdul Kadir; BAKAR, Nor Salmah

    2014-01-01

    Clival chordoma is a rare primary bone tumour that arises from the remnant of the notochord and typically occurs in older adults. Upon imaging, the tumour can be seen arising from the clivus and causes clival destruction. This usually provides insight for a diagnosis. Here we present a case of a non-enhancing, pre-pontine mass that was hypointense on T1W and hyperintense on T2W in an adolescent. No clival bone erosion was observed. Based on the age group, imaging findings, and lack of clival erosion, a provisional diagnosis of epidermoid cyst was made and the tumour was resected. This patient was eventually diagnosed with a clival chordoma based on histopathological examination. PMID:25977639

  14. Feature detection in satellite images using neural network technology

    NASA Technical Reports Server (NTRS)

    Augusteijn, Marijke F.; Dimalanta, Arturo S.

    1992-01-01

    A feasibility study of automated classification of satellite images is described. Satellite images were characterized by the textures they contain. In particular, the detection of cloud textures was investigated. The method of second-order gray level statistics, using co-occurrence matrices, was applied to extract feature vectors from image segments. Neural network technology was employed to classify these feature vectors. The cascade-correlation architecture was successfully used as a classifier. The use of a Kohonen network was also investigated but this architecture could not reliably classify the feature vectors due to the complicated structure of the classification problem. The best results were obtained when data from different spectral bands were fused.

  15. Content-based image retrieval by feature point matching

    NASA Astrophysics Data System (ADS)

    Hsu, Chiou-Ting; Wu, Ya-Ting; Chen, Arbee L. P.

    2001-01-01

    With the advance of multimedia technologies and the explosive expansion of the World Wide Web, the volume of image and video data increases rapidly. An efficient and effective multimedia data retrieval technique is needed. In this paper, we propose an approach based on feature points for the content-based image retrieval. The feature points extracted from the multiresolution representation of the query image and database image are first matched to determine the matching pairs. Then, the marching pairs are classified into groups. Finally, two similarity measurements based on different similarity requirements are proposed to compute the similarity degree. We perform a series of experiments to study the characteristics of this approach, and compare with the region-based approach on similar-shot sequence retrieval. The comparison shows the superiority of this approach.

  16. Content-based image retrieval by feature point matching

    NASA Astrophysics Data System (ADS)

    Hsu, Chiou-Ting; Wu, Ya-Ting; Chen, Arbee L.

    2000-12-01

    With the advance of multimedia technologies and the explosive expansion of the World Wide Web, the volume of image and video data increases rapidly. An efficient and effective multimedia data retrieval technique is needed. In this paper, we propose an approach based on feature points for the content-based image retrieval. The feature points extracted from the multiresolution representation of the query image and database image are first matched to determine the matching pairs. Then, the marching pairs are classified into groups. Finally, two similarity measurements based on different similarity requirements are proposed to compute the similarity degree. We perform a series of experiments to study the characteristics of this approach, and compare with the region-based approach on similar-shot sequence retrieval. The comparison shows the superiority of this approach.

  17. Dermoscopy analysis of RGB-images based on comparative features

    NASA Astrophysics Data System (ADS)

    Myakinin, Oleg O.; Zakharov, Valery P.; Bratchenko, Ivan A.; Artemyev, Dmitry N.; Neretin, Evgeny Y.; Kozlov, Sergey V.

    2015-09-01

    In this paper, we propose an algorithm for color and texture analysis for dermoscopic images of human skin based on Haar wavelets, Local Binary Patterns (LBP) and Histogram Analysis. This approach is a modification of «7-point checklist» clinical method. Thus, that is an "absolute" diagnostic method because one is using only features extracted from tumor's ROI (Region of Interest), which can be selected manually and/or using a special algorithm. We propose additional features extracted from the same image for comparative analysis of tumor and healthy skin. We used Euclidean distance, Cosine similarity, and Tanimoto coefficient as comparison metrics between color and texture features extracted from tumor's and healthy skin's ROI separately. A classifier for separating melanoma images from other tumors has been built by SVM (Support Vector Machine) algorithm. Classification's errors with and without comparative features between skin and tumor have been analyzed. Significant increase of recognition quality with comparative features has been demonstrated. Moreover, we analyzed two modes (manual and automatic) for ROI selecting on tumor and healthy skin areas. We have reached 91% of sensitivity using comparative features in contrast with 77% of sensitivity using the only "absolute" method. The specificity was the invariable (94%) in both cases.

  18. Autonomous Identification of Dominant Landscape Features in Planetary Images

    NASA Astrophysics Data System (ADS)

    Gibbens, M. J.; Cook, A. C.

    2005-08-01

    In planetary science, image analysis is mostly a manual process, with much investigative work being carried out by the inspection of either hardcopy photographs or digital imagery. However, due to the sheer enormity of the image databases being acquired by modern planetary missions (such as +100,000 MOC images), human analysis of the data in its entirety is no longer a practical consideration. In response we are developing automated preliminary image inspection techniques to help identify and catalog features of interest in the presently overwhelming data volume. From a single remote sensing image, we can construct a multiresolution scale-space representation. Dominant landscape features can be identified as continuities within the scale-space. We present detection results for marker controlled watershed segmentation of both linear (LSS) and non-linear scale spaces (NSS). Dominant landscape objects have been identified manually by 16 domain experts within a small but varied collection of images, which includes MER (HRSC), MGS (MOC), Clementine (UVVIS) and Galileo (SSI) image data. We use precision-recall curves to evaluate the trade off between accuracy and noise as the segmentation thresholds vary for the two algorithms. We have found that the consistency of features identified manually by humans varies significantly for different images. Both algorithms identify more true positives than the human average (24%), with the LSS based algorithm (43%) significantly outperforming the NSS (27%). This suggests that either algorithm could be used to correctly identify as many features as a human given the same task. Both algorithms also identify more false positives (40%) than the human average (22%). This is considered an acceptable false alarm rate for our purpose, although we hope improvements can be made. Future work will focus on utilising automatically extracted landscape objects to construct a taxonomy based on visual similarity, facilitating detailed analysis of

  19. Imaging features of poorly controlled congenital adrenal hyperplasia in adults

    PubMed Central

    Sherlock, M; Healy, N A; Doody, O; Govender, P; Torreggiani, W C

    2015-01-01

    Congenital adrenal hyperplasia (CAH) is a genetic autosomal recessive condition most frequently as a result of a mutation in the 21-hydroxylase enzyme gene. Patients with poorly controlled CAH can manifest characteristic imaging findings as a result of adrenocorticotrophic hormone stimulation or the effects of cortisol precursor excess on various target organs. We present a spectrum of imaging findings encountered in adult patients with poorly treated CAH, with an emphasis on radiological features and their clinical relevance. PMID:26133223

  20. Atypical Trigeminal Neuralgia Secondary to Meningioma

    PubMed Central

    Niwant, Premeshwar; Motwani, Mukta; Naik, Sushil

    2015-01-01

    Trigeminal neuralgia is a disorder of the fifth cranial nerve that causes episodes of intense, stabbing, electric shock-like pain that lasts from few seconds to few minutes in the areas of the face where the branches of the nerve are distributed. More than one nerve branch can be affected by the disorder. We report an unusual case of trigeminal neuralgia affecting right side of face presenting atypical features of neuralgia and not responding to the usual course of treatment. The magnetic resonance imaging study of brain revealed a large extra-axial mass involving right cerebellopontine angle region causing moderate pressure effect on trigeminal nerve and brain stem. The aim of this case report is to show a tumor of cerebellopontine angle, presenting clinically as atypical trigeminal neuralgia. PMID:26664753

  1. Feature analysis for detecting people from remotely sensed images

    NASA Astrophysics Data System (ADS)

    Sirmacek, Beril; Reinartz, Peter

    2013-01-01

    We propose a novel approach using airborne image sequences for detecting dense crowds and individuals. Although airborne images of this resolution range are not enough to see each person in detail, we can still notice a change of color and intensity components of the acquired image in the location where a person exists. Therefore, we propose a local feature detection-based probabilistic framework to detect people automatically. Extracted local features behave as observations of the probability density function (PDF) of the people locations to be estimated. Using an adaptive kernel density estimation method, we estimate the corresponding PDF. First, we use estimated PDF to detect boundaries of dense crowds. After that, using background information of dense crowds and previously extracted local features, we detect other people in noncrowd regions automatically for each image in the sequence. To test our crowd and people detection algorithm, we use airborne images taken over Munich during the Oktoberfest event, two different open-air concerts, and an outdoor festival. In addition, we apply tests on GeoEye-1 satellite images. Our experimental results indicate possible use of the algorithm in real-life mass events.

  2. Scene classification of infrared images based on texture feature

    NASA Astrophysics Data System (ADS)

    Zhang, Xiao; Bai, Tingzhu; Shang, Fei

    2008-12-01

    Scene Classification refers to as assigning a physical scene into one of a set of predefined categories. Utilizing the method texture feature is good for providing the approach to classify scenes. Texture can be considered to be repeating patterns of local variation of pixel intensities. And texture analysis is important in many applications of computer image analysis for classification or segmentation of images based on local spatial variations of intensity. Texture describes the structural information of images, so it provides another data to classify comparing to the spectrum. Now, infrared thermal imagers are used in different kinds of fields. Since infrared images of the objects reflect their own thermal radiation, there are some shortcomings of infrared images: the poor contrast between the objectives and background, the effects of blurs edges, much noise and so on. Because of these shortcomings, it is difficult to extract to the texture feature of infrared images. In this paper we have developed an infrared image texture feature-based algorithm to classify scenes of infrared images. This paper researches texture extraction using Gabor wavelet transform. The transformation of Gabor has excellent capability in analysis the frequency and direction of the partial district. Gabor wavelets is chosen for its biological relevance and technical properties In the first place, after introducing the Gabor wavelet transform and the texture analysis methods, the infrared images are extracted texture feature by Gabor wavelet transform. It is utilized the multi-scale property of Gabor filter. In the second place, we take multi-dimensional means and standard deviation with different scales and directions as texture parameters. The last stage is classification of scene texture parameters with least squares support vector machine (LS-SVM) algorithm. SVM is based on the principle of structural risk minimization (SRM). Compared with SVM, LS-SVM has overcome the shortcoming of

  3. Identification and Quantification Soil Redoximorphic Features by Digital Image Processing

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Soil redoximorphic features (SRFs) have provided scientists and land managers with insight into relative soil moisture for approximately 60 years. The overall objective of this study was to develop a new method of SRF identification and quantification from soil cores using a digital camera and imag...

  4. Detection of fungal damaged popcorn using image property covariance features

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Covariance-matrix-based features were applied to the detection of popcorn infected by a fungus that cause a symptom called “blue-eye.” This infection of popcorn kernels causes economic losses because of their poor appearance and the frequently disagreeable flavor of the popped kernels. Images of ker...

  5. Visual Pattern Analysis in Histopathology Images Using Bag of Features

    NASA Astrophysics Data System (ADS)

    Cruz-Roa, Angel; Caicedo, Juan C.; González, Fabio A.

    This paper presents a framework to analyse visual patterns in a collection of medical images in a two stage procedure. First, a set of representative visual patterns from the image collection is obtained by constructing a visual-word dictionary under a bag-of-features approach. Second, an analysis of the relationships between visual patterns and semantic concepts in the image collection is performed. The most important visual patterns for each semantic concept are identified using correlation analysis. A matrix visualization of the structure and organization of the image collection is generated using a cluster analysis. The experimental evaluation was conducted on a histopathology image collection and results showed clear relationships between visual patterns and semantic concepts, that in addition, are of easy interpretation and understanding.

  6. Atypical manifestations of dengue fever.

    PubMed

    Pawaria, Arti; Mishra, Devendra; Juneja, Monica; Meena, Jagdish

    2014-06-01

    We reviewed case records of 40 in-patients (22 boys) with serologically confirmed dengue fever between 1st October and 30th November, 2013. Severe dengue was seen in 30, out of which 12 (30%) had compensated shock. Splenomegaly (6,15%) and encephalopathy (4,10%) were the commonest atypical features. Atypical manifestations of dengue fever were more common than that reported in the past.

  7. Assessment of femur geometrical parameters using EOS™ imaging technology in patients with atypical femur fractures; preliminary results.

    PubMed

    Morin, Suzanne N; Wall, Michelle; Belzile, Etienne L; Godbout, Benoit; Moser, Thomas P; Michou, Laëtitia; Ste-Marie, Louis-Georges; de Guise, Jacques A; Rahme, Elham; Brown, Jacques P

    2016-02-01

    Atypical femur fractures (AFF) arise in the subtrochanteric and diaphyseal regions. Because of this unique distribution, we hypothesized that patients with AFF demonstrate specific geometrical variations of their lower limb whereby baseline tensile forces applied to the lateral cortex are higher and might favor the appearance of these rare stress fractures, when exposed to bisphosphonates. Using the low irradiation 2D-3D X-ray scanner EOS™ imaging technology we aimed to characterize and compare femur geometric parameters between women who sustained bisphosphonate-associated AFF and those who had experienced similar duration of exposure to bisphosphonates but did not sustain fractures. Conditional logistic regression models were constructed to estimate the association between selected geometric parameters and the occurrence of AFF. We identified 16 Caucasian women with AFF and recruited 16 ethnicity-, sex-, age-, height- and cumulative bisphosphonate exposure-matched controls from local osteoporosis clinics. Compared to controls, those with AFF had more lateral femur bowing (-3.2° SD [3.4] versus -0.8° SD [1.9] p=0.02). In regression analysis, lateral femur bowing was associated with the risk of AFF (aOR 1.54; 95% CI 1.04-2.28, p=0.03). Women who sustained a subtrochanteric AFF demonstrated a lesser femoral neck shaft angle (varus geometry) than those with a fracture at a diaphyseal site (121.9 [3.6]° versus 127.6 [7.2]°, p=0.07), whereas femur bowing was more prominent in those with a diaphyseal fracture compared to those with a subtrochanteric fracture (-4.3 [3.2]° versus -0.9 [2.7]°, p=0.07). Our analyses support that subjects with AFF exhibit femoral geometry parameters that result in higher tensile mechanical load on the lateral femur. This may play a critical role in the pathogenesis of AFF and requires further evaluation in a larger size population.

  8. Assessment of femur geometrical parameters using EOS™ imaging technology in patients with atypical femur fractures; preliminary results.

    PubMed

    Morin, Suzanne N; Wall, Michelle; Belzile, Etienne L; Godbout, Benoit; Moser, Thomas P; Michou, Laëtitia; Ste-Marie, Louis-Georges; de Guise, Jacques A; Rahme, Elham; Brown, Jacques P

    2016-02-01

    Atypical femur fractures (AFF) arise in the subtrochanteric and diaphyseal regions. Because of this unique distribution, we hypothesized that patients with AFF demonstrate specific geometrical variations of their lower limb whereby baseline tensile forces applied to the lateral cortex are higher and might favor the appearance of these rare stress fractures, when exposed to bisphosphonates. Using the low irradiation 2D-3D X-ray scanner EOS™ imaging technology we aimed to characterize and compare femur geometric parameters between women who sustained bisphosphonate-associated AFF and those who had experienced similar duration of exposure to bisphosphonates but did not sustain fractures. Conditional logistic regression models were constructed to estimate the association between selected geometric parameters and the occurrence of AFF. We identified 16 Caucasian women with AFF and recruited 16 ethnicity-, sex-, age-, height- and cumulative bisphosphonate exposure-matched controls from local osteoporosis clinics. Compared to controls, those with AFF had more lateral femur bowing (-3.2° SD [3.4] versus -0.8° SD [1.9] p=0.02). In regression analysis, lateral femur bowing was associated with the risk of AFF (aOR 1.54; 95% CI 1.04-2.28, p=0.03). Women who sustained a subtrochanteric AFF demonstrated a lesser femoral neck shaft angle (varus geometry) than those with a fracture at a diaphyseal site (121.9 [3.6]° versus 127.6 [7.2]°, p=0.07), whereas femur bowing was more prominent in those with a diaphyseal fracture compared to those with a subtrochanteric fracture (-4.3 [3.2]° versus -0.9 [2.7]°, p=0.07). Our analyses support that subjects with AFF exhibit femoral geometry parameters that result in higher tensile mechanical load on the lateral femur. This may play a critical role in the pathogenesis of AFF and requires further evaluation in a larger size population. PMID:26541215

  9. Image Recognition and Feature Detection in Solar Physics

    NASA Astrophysics Data System (ADS)

    Martens, Petrus C.

    2012-05-01

    The Solar Dynamics Observatory (SDO) data repository will dwarf the archives of all previous solar physics missions put together. NASA recognized early on that the traditional methods of analyzing the data -- solar scientists and grad students in particular analyzing the images by hand -- would simply not work and tasked our Feature Finding Team (FFT) with developing automated feature recognition modules for solar events and phenomena likely to be observed by SDO. Having these metadata available on-line will enable solar scientist to conduct statistical studies involving large sets of events that would be impossible now with traditional means. We have followed a two-track approach in our project: we have been developing some existing task-specific solar feature finding modules to be "pipe-line" ready for the stream of SDO data, plus we are designing a few new modules. Secondly, we took it upon us to develop an entirely new "trainable" module that would be capable of identifying different types of solar phenomena starting from a limited number of user-provided examples. Both approaches are now reaching fruition, and I will show examples and movies with results from several of our feature finding modules. In the second part of my presentation I will focus on our “trainable” module, which is the most innovative in character. First, there is the strong similarity between solar and medical X-ray images with regard to their texture, which has allowed us to apply some advances made in medical image recognition. Second, we have found that there is a strong similarity between the way our trainable module works and the way our brain recognizes images. The brain can quickly recognize similar images from key characteristics, just as our code does. We conclude from that that our approach represents the beginning of a more human-like procedure for computer image recognition.

  10. Single Image Superresolution via Directional Group Sparsity and Directional Features.

    PubMed

    Li, Xiaoyan; He, Hongjie; Wang, Ruxin; Tao, Dacheng

    2015-09-01

    Single image superresolution (SR) aims to construct a high-resolution version from a single low-resolution (LR) image. The SR reconstruction is challenging because of the missing details in the given LR image. Thus, it is critical to explore and exploit effective prior knowledge for boosting the reconstruction performance. In this paper, we propose a novel SR method by exploiting both the directional group sparsity of the image gradients and the directional features in similarity weight estimation. The proposed SR approach is based on two observations: 1) most of the sharp edges are oriented in a limited number of directions and 2) an image pixel can be estimated by the weighted averaging of its neighbors. In consideration of these observations, we apply the curvelet transform to extract directional features which are then used for region selection and weight estimation. A combined total variation regularizer is presented which assumes that the gradients in natural images have a straightforward group sparsity structure. In addition, a directional nonlocal means regularization term takes pixel values and directional information into account to suppress unwanted artifacts. By assembling the designed regularization terms, we solve the SR problem of an energy function with minimal reconstruction error by applying a framework of templates for first-order conic solvers. The thorough quantitative and qualitative results in terms of peak signal-to-noise ratio, structural similarity, information fidelity criterion, and preference matrix demonstrate that the proposed approach achieves higher quality SR reconstruction than the state-of-the-art algorithms.

  11. BRAF mutation is associated with a specific cell-type with features suggestive of senescence in ovarian serous borderline (atypical proliferative) tumors

    PubMed Central

    Zeppernick, Felix; Ardighieri, Laura; Hannibal, Charlotte G.; Vang, Russell; Junge, Jette; Kjaer, Susanne K.; Zhang, Rugang; Kurman, Robert J.; Shih, Ie-Ming

    2014-01-01

    Serous borderline tumor (SBT) also known as atypical proliferative serous tumor (APST) is the precursor of ovarian low-grade serous carcinoma (LGSC). In this study, we correlated the morphologic and immunohistochemical phenotypes of 71 APSTs and 18 LGSCs with the mutational status of KRAS and BRAF, the most common molecular genetic changes in these neoplasms. A subset of cells characterized by abundant eosinophilic cytoplasm (EC), discrete cell borders and bland nuclei was identified in all (100%) 25 BRAF mutated APSTs but in only 5 (10%) of 46 APSTs without BRAF mutations (p<0.0001). Among the 18 LGSCs, EC cells were found in only 2 and both contained BRAF mutations. The EC cells were present admixed with cuboidal and columnar cells lining the papillae and appeared to be budding from the surface, resulting in individual cells and clusters of detached cells “floating” above the papillae. Immunohistochemistry showed that the EC cells always expressed p16, a senescence-associated marker, and had a significantly lower Ki-67 labeling index than adjacent cuboidal and columnar cells (p=0.02). In vitro studies supported the interpretation that these cells were undergoing senescence as the same morphologic features could be reproduced in cultured epithelial cells by ectopic expression of BRAFV600E. Senescence was further established by markers such as SA-β-gal staining, expression of p16 and p21, and reduction in DNA synthesis. In conclusion, this study sheds light on the pathogenesis of this unique group of ovarian tumors by showing that BRAF mutation is associated with cellular senescence and the presence of a specific cell type characterized by abundant eosinophilic cytoplasm. This “oncogene-induced senescence” phenotype may represent a mechanism that prevents impedes progression of APSTs to LGSC. PMID:25188864

  12. Feature-aided multiple target tracking in the image plane

    NASA Astrophysics Data System (ADS)

    Brown, Andrew P.; Sullivan, Kevin J.; Miller, David J.

    2006-05-01

    Vast quantities of EO and IR data are collected on airborne platforms (manned and unmanned) and terrestrial platforms (including fixed installations, e.g., at street intersections), and can be exploited to aid in the global war on terrorism. However, intelligent preprocessing is required to enable operator efficiency and to provide commanders with actionable target information. To this end, we have developed an image plane tracker which automatically detects and tracks multiple targets in image sequences using both motion and feature information. The effects of platform and camera motion are compensated via image registration, and a novel change detection algorithm is applied for accurate moving target detection. The contiguous pixel blob on each moving target is segmented for use in target feature extraction and model learning. Feature-based target location measurements are used for tracking through move-stop-move maneuvers, close target spacing, and occlusion. Effective clutter suppression is achieved using joint probabilistic data association (JPDA), and confirmed target tracks are indicated for further processing or operator review. In this paper we describe the algorithms implemented in the image plane tracker and present performance results obtained with video clips from the DARPA VIVID program data collection and from a miniature unmanned aerial vehicle (UAV) flight.

  13. Quantitative imaging features to predict cancer status in lung nodules

    NASA Astrophysics Data System (ADS)

    Liu, Ying; Balagurunathan, Yoganand; Atwater, Thomas; Antic, Sanja; Li, Qian; Walker, Ronald; Smith, Gary T.; Massion, Pierre P.; Schabath, Matthew B.; Gillies, Robert J.

    2016-03-01

    Background: We propose a systematic methodology to quantify incidentally identified lung nodules based on observed radiological traits on a point scale. These quantitative traits classification model was used to predict cancer status. Materials and Methods: We used 102 patients' low dose computed tomography (LDCT) images for this study, 24 semantic traits were systematically scored from each image. We built a machine learning classifier in cross validation setting to find best predictive imaging features to differentiate malignant from benign lung nodules. Results: The best feature triplet to discriminate malignancy was based on long axis, concavity and lymphadenopathy with average AUC of 0.897 (Accuracy of 76.8%, Sensitivity of 64.3%, Specificity of 90%). A similar semantic triplet optimized on Sensitivity/Specificity (Youden's J index) included long axis, vascular convergence and lymphadenopathy which had an average AUC of 0.875 (Accuracy of 81.7%, Sensitivity of 76.2%, Specificity of 95%). Conclusions: Quantitative radiological image traits can differentiate malignant from benign lung nodules. These semantic features along with size measurement enhance the prediction accuracy.

  14. Incorporating global information in feature-based multimodal image registration

    NASA Astrophysics Data System (ADS)

    Li, Yong; Stevenson, Robert

    2014-03-01

    A multimodal image registration framework based on searching the best matched keypoints and the incorporation of global information is proposed. It comprises two key elements: keypoint detection and an iterative process. Keypoints are detected from both the reference and test images. For each test keypoint, a number of reference keypoints are chosen as mapping candidates. A triplet of keypoint mappings determine an affine transformation that is evaluated using a similarity metric between the reference image and the transformed test image by the determined transformation. An iterative process is conducted on triplets of keypoint mappings, keeping track of the best matched reference keypoint. Random sample consensus and mutual information are applied to eliminate outlier keypoint mappings. The similarity metric is defined to be the number of overlapped edge pixels over the entire images, allowing for global information to be incorporated in the evaluation of triplets of mappings. The performance of the framework is investigated with keypoints extracted by scale invariant feature transform and partial intensity invariant feature descriptor. Experimental results show that the proposed framework can provide more accurate registration than existing methods.

  15. Comparison of additive image fusion vs. feature-level image fusion techniques for enhanced night driving

    NASA Astrophysics Data System (ADS)

    Bender, Edward J.; Reese, Colin E.; Van Der Wal, Gooitzen S.

    2003-02-01

    The Night Vision & Electronic Sensors Directorate (NVESD) has conducted a series of image fusion evaluations under the Head-Tracked Vision System (HTVS) program. The HTVS is a driving system for both wheeled and tracked military vehicles, wherein dual-waveband sensors are directed in a more natural head-slewed imaging mode. The HTVS consists of thermal and image-intensified TV sensors, a high-speed gimbal, a head-mounted display, and a head tracker. A series of NVESD field tests over the past two years has investigated the degree to which additive (A+B) image fusion of these sensors enhances overall driving performance. Additive fusion employs a single (but user adjustable) fractional weighting for all the features of each sensor's image. More recently, NVESD and Sarnoff Corporation have begun a cooperative effort to evaluate and refine Sarnoff's "feature-level" multi-resolution (pyramid) algorithms for image fusion. This approach employs digital processing techniques to select at each image point only the sensor with the strongest features, and to utilize only those features to reconstruct the fused video image. This selection process is performed simultaneously at multiple scales of the image, which are combined to form the reconstructed fused image. All image fusion techniques attempt to combine the "best of both sensors" in a single image. Typically, thermal sensors are better for detecting military threats and targets, while image-intensified sensors provide more natural scene cues and detect cultural lighting. This investigation will address the differences between additive fusion and feature-level image fusion techniques for enhancing the driver's overall situational awareness.

  16. Comparison of Magnetic Resonance Imaging Findings between Pathologically Proven Cases of Atypical Tubercular Spine and Tumour Metastasis: A Retrospective Study in 40 Patients

    PubMed Central

    Khalid, Mohd; Sabir, Aamir Bin; Khalid, Saifullah

    2016-01-01

    Study Design Retrospective study. Purpose To note the magnetic resonance imaging (MRI) differences between pathologically proven cases of atypical spinal tuberculosis and spinal metastasis in 40 cases. Overview of Literature Spinal tuberculosis, or Pott's spine, constitutes less than 1% of all cases of tuberculosis and can be associated with a neurologic deficit. Breast, prostate and lung cancer are responsible for more than 80% of metastatic bone disease cases, and spine is the most common site of bone metastasis. Thus, early diagnosis and prompt management of these pathologies are essential in preventing various complications. Methods We retrospectively reviewed 40 cases of atypical tuberculosis and metastasis affecting the spine from the year 2012 to 2014, with 20 cases each that were proven by histopathological examination. MR imaging was performed on 1.5 T MR-Scanner (Magnetom Avanto, Siemens) utilizing standard surface coils of spine with contrast injection. Chi-square test was used for determining the statistical significance and p-values were calculated. Results The most common site of involvement was the thoracic spine, seen in 85% cases of metastasis and 65% cases of Pott's spine (p=0.144). The mean age of patients with tubercular spine was found to be 40 years and that of metastatic spine was 56 years. The following MR imaging findings showed statistical significance (p<0.05): combined vertebral body and posterior elements involvement, skip lesions, solitary lesion, intra-spinal lesions, concentric collapse, abscess formation and syrinx formation. Conclusions Tuberculosis should be considered in the differential diagnosis of various spinal lesions including metastasis, fungal spondylodiskitis, sarcoidosis and lymphoma, particularly in endemic countries. Spinal tuberculosis is considered one of the great mimickers of disease as it could present in a variety of typical and atypical patterns, so proper imaging must be performed in order to facilitate

  17. Characteristic imaging features of body packers: a pictorial essay.

    PubMed

    Ab Hamid, Suzana; Abd Rashid, Saiful Nizam; Mohd Saini, Suraini

    2012-06-01

    The drug-trafficking business has risen tremendously because of the current increased demand for illegal narcotics. The smugglers conceal the drugs in their bodies (body packers) in order to bypass the tight security at international borders. A suspected body packer will normally be sent to the hospital for imaging investigations to confirm the presence of drugs in the body. Radiologists, therefore, need to be familiar with and able to identify drug packets within the human body because they shoulder the legal responsibilities. This pictorial essay describes the characteristic imaging features of drug packets within the gastrointestinal tract. PMID:22415809

  18. Feature-based eye corner detection from static images

    NASA Astrophysics Data System (ADS)

    Xia, Haiying; Yan, Guoping; You, Chao

    2009-10-01

    Eye corner detection is important for eye extraction, face normalization, other facial landmark extraction and so on. We present a feature-based method for eye corner detection from static images in this paper. This method is capable of locating eye corners automatically. The process of eye corner detection is divided into two stages: classifier training and classifier application. For training, two classifiers trained by AdaBoost with Haar-like features, are skillfully designed to detect inner eye corners and outer eye corners. Then, two classifiers are applied to input images to search targets. Eye corners are finally located according to two eye models from targets. Experimental results tested on BioID face database and our own database demonstrate that our method obtains a high accuracy under clutter conditions.

  19. Scaling features of texts, images and time series

    NASA Astrophysics Data System (ADS)

    Pavlov, Alexey N.; Ebeling, Werner; Molgedey, Lutz; Ziganshin, Amir R.; Anishchenko, Vadim S.

    2001-11-01

    In the given paper, we consider the scaling features of long letter sequences like human writings, discretized images and discretized financial data. Using several approaches we show that the symbolic strings and time series being analyzed have a complex multiscale structure and demonstrate different scalings for large and small fluctuations. We discuss complex phenomena in the scaling behavior of partition functions in the case of high frequency DAX-future data.

  20. Nearest feature line embedding approach to hyperspectral image classification

    NASA Astrophysics Data System (ADS)

    Chang, Yang-Lang; Liu, Jin-Nan; Han, Chin-Chuan; Chen, Ying-Nong; Hsieh, Tung-Ju; Huang, Bormin

    2012-10-01

    In this paper, a nearest feature line (NFL) embedding transformation is proposed for dimension reduction of hyperspectral image (HSI). Eigenspace projection approaches are generally used for feature extraction of HSI in remote sensing image classification. In order to improve the classification accuracy, the feature vectors of high dimensions are reduced to the low dimensionalities by the effective projection transformation. Similarly, the proposed NFL measurement is embedded into the transformation during the discriminant analysis stage instead of the matching stage. The class separability, neighborhood structure preservation, and NFL measurement are also simultaneously considered to find the effective and discriminating transformation in eigenspaces for image classification. The nearest neighbor classifier is used to show the discriminative performance. The proposed NFL embedding transformation is compared with several conventional state-of-the-art algorithms. It was evaluated by the AVIRIS data sets of Northwest Tippecanoe County. Experimental results have demonstrated that NFL embedding method is an effective transformation for dimension reduction in land cover classification of earth remote sensing.

  1. MR imaging features of focal liver lesions in Wilson disease.

    PubMed

    Dohan, Anthony; Vargas, Ottavia; Dautry, Raphael; Guerrache, Youcef; Woimant, France; Hamzi, Lounis; Boudiaf, Mourad; Poujois, Aurelia; Faraoun, Sid Ahmed; Soyer, Philippe

    2016-09-01

    Hepatic involvement in Wilson disease (WD) manifests as a diffuse chronic disease in the majority of patients. However, in a subset of patients focal liver lesions may develop, presenting with a wide range of imaging features. The majority of focal liver lesions in patients with WD are benign nodules, but there are reports that have described malignant liver tumors or dysplastic nodules in these patients. Because of the possibility of malignant transformation of liver nodules, major concerns have been raised with respect to the management and follow-up of patients with WD in whom focal liver lesions have been identified. The assessment of liver involvement in patients with WD is generally performed with ultrasonography. However, ultrasonography conveys limited specificity so that magnetic resonance (MR) imaging is often performed to improve lesion characterization. This review was performed to illustrate the spectrum of MR imaging features of focal liver lesions that develop in patients with WD. It is assumed that familiarity with the MR imaging presentation of focal liver lesions in WD may help clarify the actual nature of hepatic nodules in patients with this condition. PMID:27116011

  2. MR imaging features of focal liver lesions in Wilson disease.

    PubMed

    Dohan, Anthony; Vargas, Ottavia; Dautry, Raphael; Guerrache, Youcef; Woimant, France; Hamzi, Lounis; Boudiaf, Mourad; Poujois, Aurelia; Faraoun, Sid Ahmed; Soyer, Philippe

    2016-09-01

    Hepatic involvement in Wilson disease (WD) manifests as a diffuse chronic disease in the majority of patients. However, in a subset of patients focal liver lesions may develop, presenting with a wide range of imaging features. The majority of focal liver lesions in patients with WD are benign nodules, but there are reports that have described malignant liver tumors or dysplastic nodules in these patients. Because of the possibility of malignant transformation of liver nodules, major concerns have been raised with respect to the management and follow-up of patients with WD in whom focal liver lesions have been identified. The assessment of liver involvement in patients with WD is generally performed with ultrasonography. However, ultrasonography conveys limited specificity so that magnetic resonance (MR) imaging is often performed to improve lesion characterization. This review was performed to illustrate the spectrum of MR imaging features of focal liver lesions that develop in patients with WD. It is assumed that familiarity with the MR imaging presentation of focal liver lesions in WD may help clarify the actual nature of hepatic nodules in patients with this condition.

  3. 3D lung image retrieval using localized features

    NASA Astrophysics Data System (ADS)

    Depeursinge, Adrien; Zrimec, Tatjana; Busayarat, Sata; Müller, Henning

    2011-03-01

    The interpretation of high-resolution computed tomography (HRCT) images of the chest showing disorders of the lung tissue associated with interstitial lung diseases (ILDs) is time-consuming and requires experience. Whereas automatic detection and quantification of the lung tissue patterns showed promising results in several studies, its aid for the clinicians is limited to the challenge of image interpretation, letting the radiologists with the problem of the final histological diagnosis. Complementary to lung tissue categorization, providing visually similar cases using content-based image retrieval (CBIR) is in line with the clinical workflow of the radiologists. In a preliminary study, a Euclidean distance based on volume percentages of five lung tissue types was used as inter-case distance for CBIR. The latter showed the feasibility of retrieving similar histological diagnoses of ILD based on visual content, although no localization information was used for CBIR. However, to retrieve and show similar images with pathology appearing at a particular lung position was not possible. In this work, a 3D localization system based on lung anatomy is used to localize low-level features used for CBIR. When compared to our previous study, the introduction of localization features allows improving early precision for some histological diagnoses, especially when the region of appearance of lung tissue disorders is important.

  4. Dual-pass feature extraction on human vessel images.

    PubMed

    Hernandez, W; Grimm, S; Andriantsimiavona, R

    2014-06-01

    We present a novel algorithm for the extraction of cavity features on images of human vessels. Fat deposits in the inner wall of such structure introduce artifacts, and regions in the images captured invalidating the usual assumption of an elliptical model which makes the process of extracting the central passage effectively more difficult. Our approach was designed to cope with these challenges and extract the required image features in a fully automated, accurate, and efficient way using two stages: the first allows to determine a bounding segmentation mask to prevent major leakages from pixels of the cavity area by using a circular region fill that operates as a paint brush followed by Principal Component Analysis with auto correction; the second allows to extract a precise cavity enclosure using a micro-dilation filter and an edge-walking scheme. The accuracy of the algorithm has been tested using 30 computed tomography angiography scans of the lower part of the body containing different degrees of inner wall distortion. The results were compared to manual annotations from a specialist resulting in sensitivity around 98 %, false positive rate around 8 %, and positive predictive value around 93 %. The average execution time was 24 and 18 ms on two types of commodity hardware over sections of 15 cm of length (approx. 1 ms per contour) which makes it more than suitable for use in interactive software applications. Reproducibility tests were also carried out with synthetic images showing no variation for the computed diameters against the theoretical measure.

  5. Imaging features of thoracic metastases from gynecologic neoplasms.

    PubMed

    Martínez-Jiménez, Santiago; Rosado-de-Christenson, Melissa L; Walker, Christopher M; Kunin, Jeffery R; Betancourt, Sonia L; Shoup, Brenda L; Pettavel, Paul P

    2014-10-01

    Gynecologic malignancies are a heterogeneous group of common neoplasms and represent the fourth most common malignancy in women. Thoracic metastases exhibit various imaging patterns and are usually associated with locally invasive primary neoplasms with intra-abdominal spread. However, thoracic involvement may also occur many months to years after initial diagnosis or as an isolated finding in patients without evidence of intra-abdominal neoplastic involvement. Thoracic metastases from endometrial carcinoma typically manifest as pulmonary nodules and lymphadenopathy. Thoracic metastases from ovarian cancer often manifest with small pleural effusions and subtle pleural nodules. Thoracic metastases to the lungs, lymph nodes, and pleura may also exhibit calcification and mimic granulomatous disease. Metastases from fallopian tube carcinomas exhibit imaging features identical to those of ovarian cancers. Most cervical cancers are of squamous histology, and while solid pulmonary metastases are more common, cavitary metastases occur with some frequency. Metastatic choriocarcinoma to the lung characteristically manifests with solid pulmonary nodules. Some pulmonary metastases from gynecologic malignancies exhibit characteristic features such as cavitation (in squamous cell cervical cancer) and the "halo" sign (in hemorrhagic metastatic choriocarcinoma) at computed tomography (CT). However, metastases from common gynecologic malignancies may be subtle and indolent and may mimic benign conditions such as intrapulmonary lymph nodes and remote granulomatous disease. Therefore, radiologists should consider the presence of locoregional disease as well as elevated tumor marker levels when interpreting imaging studies because subtle imaging findings may represent metastatic disease. Positron emission tomography/CT may be helpful in identifying early locoregional and distant tumor spread. PMID:25310428

  6. Dual-pass feature extraction on human vessel images.

    PubMed

    Hernandez, W; Grimm, S; Andriantsimiavona, R

    2014-06-01

    We present a novel algorithm for the extraction of cavity features on images of human vessels. Fat deposits in the inner wall of such structure introduce artifacts, and regions in the images captured invalidating the usual assumption of an elliptical model which makes the process of extracting the central passage effectively more difficult. Our approach was designed to cope with these challenges and extract the required image features in a fully automated, accurate, and efficient way using two stages: the first allows to determine a bounding segmentation mask to prevent major leakages from pixels of the cavity area by using a circular region fill that operates as a paint brush followed by Principal Component Analysis with auto correction; the second allows to extract a precise cavity enclosure using a micro-dilation filter and an edge-walking scheme. The accuracy of the algorithm has been tested using 30 computed tomography angiography scans of the lower part of the body containing different degrees of inner wall distortion. The results were compared to manual annotations from a specialist resulting in sensitivity around 98 %, false positive rate around 8 %, and positive predictive value around 93 %. The average execution time was 24 and 18 ms on two types of commodity hardware over sections of 15 cm of length (approx. 1 ms per contour) which makes it more than suitable for use in interactive software applications. Reproducibility tests were also carried out with synthetic images showing no variation for the computed diameters against the theoretical measure. PMID:24197278

  7. CFA-aware features for steganalysis of color images

    NASA Astrophysics Data System (ADS)

    Goljan, Miroslav; Fridrich, Jessica

    2015-03-01

    Color interpolation is a form of upsampling, which introduces constraints on the relationship between neighboring pixels in a color image. These constraints can be utilized to substantially boost the accuracy of steganography detectors. In this paper, we introduce a rich model formed by 3D co-occurrences of color noise residuals split according to the structure of the Bayer color filter array to further improve detection. Some color interpolation algorithms, AHD and PPG, impose pixel constraints so tight that extremely accurate detection becomes possible with merely eight features eliminating the need for model richification. We carry out experiments on non-adaptive LSB matching and the content-adaptive algorithm WOW on five different color interpolation algorithms. In contrast to grayscale images, in color images that exhibit traces of color interpolation the security of WOW is significantly lower and, depending on the interpolation algorithm, may even be lower than non-adaptive LSB matching.

  8. Multispectral image feature fusion for detecting land mines

    SciTech Connect

    Clark, G.A.; Fields, D.J.; Sherwood, R.J.

    1994-11-15

    Our system fuses information contained in registered images from multiple sensors to reduce the effect of clutter and improve the the ability to detect surface and buried land mines. The sensor suite currently consists if a camera that acquires images in sixible wavelength bands, du, dual-band infrared (5 micron and 10 micron) and ground penetrating radar. Past research has shown that it is extremely difficult to distinguish land mines from background clutter in images obtained from a single sensor. It is hypothesized, however, that information fused from a suite of various sensors is likely to provide better detection reliability, because the suite of sensors detects a variety of physical properties that are more separate in feature space. The materials surrounding the mines can include natural materials (soil, rocks, foliage, water, holes made by animals and natural processes, etc.) and some artifacts.

  9. Sesamoiditis of the cyamella: imaging features of this rare presentation.

    PubMed

    Dykes, Michael I; Vijay, Ram K P

    2014-02-01

    We report a unique case of sesamoiditis in an extremely rare accessory genicular ossicle. Common to lower primates, the cyamella or popliteus tendon sesamoid bone is usually absent in humans. A 19-year-old male sustained a twisting injury to the right knee and presented with mechanical symptoms of knee pseudo-locking. A plain radiograph of the knee illustrated the presence of a cyamella. Magnetic resonance imaging demonstrated marked bone marrow oedema of this sesamoid structure and in the adjacent popliteus tendon. To our knowledge, documented sesamoiditis of this osseous structure on radiological imaging has not been published in English literature and this case highlights the imaging features of this uncommon entity. PMID:24327263

  10. A feature-enriched completely blind image quality evaluator.

    PubMed

    Lin Zhang; Lei Zhang; Bovik, Alan C

    2015-08-01

    Existing blind image quality assessment (BIQA) methods are mostly opinion-aware. They learn regression models from training images with associated human subjective scores to predict the perceptual quality of test images. Such opinion-aware methods, however, require a large amount of training samples with associated human subjective scores and of a variety of distortion types. The BIQA models learned by opinion-aware methods often have weak generalization capability, hereby limiting their usability in practice. By comparison, opinion-unaware methods do not need human subjective scores for training, and thus have greater potential for good generalization capability. Unfortunately, thus far no opinion-unaware BIQA method has shown consistently better quality prediction accuracy than the opinion-aware methods. Here, we aim to develop an opinion-unaware BIQA method that can compete with, and perhaps outperform, the existing opinion-aware methods. By integrating the features of natural image statistics derived from multiple cues, we learn a multivariate Gaussian model of image patches from a collection of pristine natural images. Using the learned multivariate Gaussian model, a Bhattacharyya-like distance is used to measure the quality of each image patch, and then an overall quality score is obtained by average pooling. The proposed BIQA method does not need any distorted sample images nor subjective quality scores for training, yet extensive experiments demonstrate its superior quality-prediction performance to the state-of-the-art opinion-aware BIQA methods. The MATLAB source code of our algorithm is publicly available at www.comp.polyu.edu.hk/~cslzhang/IQA/ILNIQE/ILNIQE.htm.

  11. A feature-enriched completely blind image quality evaluator.

    PubMed

    Lin Zhang; Lei Zhang; Bovik, Alan C

    2015-08-01

    Existing blind image quality assessment (BIQA) methods are mostly opinion-aware. They learn regression models from training images with associated human subjective scores to predict the perceptual quality of test images. Such opinion-aware methods, however, require a large amount of training samples with associated human subjective scores and of a variety of distortion types. The BIQA models learned by opinion-aware methods often have weak generalization capability, hereby limiting their usability in practice. By comparison, opinion-unaware methods do not need human subjective scores for training, and thus have greater potential for good generalization capability. Unfortunately, thus far no opinion-unaware BIQA method has shown consistently better quality prediction accuracy than the opinion-aware methods. Here, we aim to develop an opinion-unaware BIQA method that can compete with, and perhaps outperform, the existing opinion-aware methods. By integrating the features of natural image statistics derived from multiple cues, we learn a multivariate Gaussian model of image patches from a collection of pristine natural images. Using the learned multivariate Gaussian model, a Bhattacharyya-like distance is used to measure the quality of each image patch, and then an overall quality score is obtained by average pooling. The proposed BIQA method does not need any distorted sample images nor subjective quality scores for training, yet extensive experiments demonstrate its superior quality-prediction performance to the state-of-the-art opinion-aware BIQA methods. The MATLAB source code of our algorithm is publicly available at www.comp.polyu.edu.hk/~cslzhang/IQA/ILNIQE/ILNIQE.htm. PMID:25915960

  12. Shape adaptive, robust iris feature extraction from noisy iris images.

    PubMed

    Ghodrati, Hamed; Dehghani, Mohammad Javad; Danyali, Habibolah

    2013-10-01

    In the current iris recognition systems, noise removing step is only used to detect noisy parts of the iris region and features extracted from there will be excluded in matching step. Whereas depending on the filter structure used in feature extraction, the noisy parts may influence relevant features. To the best of our knowledge, the effect of noise factors on feature extraction has not been considered in the previous works. This paper investigates the effect of shape adaptive wavelet transform and shape adaptive Gabor-wavelet for feature extraction on the iris recognition performance. In addition, an effective noise-removing approach is proposed in this paper. The contribution is to detect eyelashes and reflections by calculating appropriate thresholds by a procedure called statistical decision making. The eyelids are segmented by parabolic Hough transform in normalized iris image to decrease computational burden through omitting rotation term. The iris is localized by an accurate and fast algorithm based on coarse-to-fine strategy. The principle of mask code generation is to assign the noisy bits in an iris code in order to exclude them in matching step is presented in details. An experimental result shows that by using the shape adaptive Gabor-wavelet technique there is an improvement on the accuracy of recognition rate. PMID:24696801

  13. Magnetic Resonance Imaging Features of Adenosis in the Breast

    PubMed Central

    Gity, Masoumeh; Arabkheradmand, Ali; Shakiba, Madjid; Khademi, Yassaman; Bijan, Bijan; Sadaghiani, Mohammad Salehi; Jalali, Amir Hossein

    2015-01-01

    Purpose Adenosis lesions of the breast, including sclerosing adenosis and adenosis tumors, are a group of benign proliferative disorders that may mimic the features of malignancy on imaging. In this study, we aim to describe the features of breast adenosis lesions with suspicious or borderline findings on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Methods In our database, we identified 49 pathologically proven breast adenosis lesions for which the final assessment of the breast MRI report was classified as either category 4 (n=45) or category 5 (n=4), according to the Breast Imaging Reporting and Data System (BI-RADS) published by the American College of Radiology (ACR). The lesions had a final diagnosis of either pure adenosis (n=33, 67.3%) or mixed adenosis associated with other benign pathologies (n=16, 32.7%). Results Of the 49 adenosis lesions detected on DCE-MRI, 32 (65.3%) appeared as enhancing masses, 16 (32.7%) as nonmass enhancements, and one (2.1%) as a tiny enhancing focus. Analysis of the enhancing masses based on the ACR BI-RADS lexicon revealed that among the mass descriptors, the most common features were irregular shape in 12 (37.5%), noncircumscribed margin in 20 (62.5%), heterogeneous internal pattern in 16 (50.0%), rapid initial enhancement in 32 (100.0%), and wash-out delayed en-hancement pattern in 21 (65.6%). Of the 16 nonmass enhancing lesions, the most common descriptors included focal distribution in seven (43.8%), segmental distribution in six (37.5%), clumped internal pattern in nine (56.3%), rapid initial enhancement in 16 (100.0%), and wash-out delayed enhancement pattern in eight (50.0%). Conclusion Adenosis lesions of the breast may appear suspicious on breast MRI. Awareness of these suspi-cious-appearing features would be helpful in obviating unnecessary breast biopsies. PMID:26155296

  14. Development of an Expert System as a Diagnostic Support of Cervical Cancer in Atypical Glandular Cells, Based on Fuzzy Logics and Image Interpretation

    PubMed Central

    Domínguez Hernández, Karem R.; Aguilar Lasserre, Alberto A.; Posada Gómez, Rubén; Palet Guzmán, José A.; González Sánchez, Blanca E.

    2013-01-01

    Cervical cancer is the second largest cause of death among women worldwide. Nowadays, this disease is preventable and curable at low cost and low risk when an accurate diagnosis is done in due time, since it is the neoplasm with the highest prevention potential. This work describes the development of an expert system able to provide a diagnosis to cervical neoplasia (CN) precursor injuries through the integration of fuzzy logics and image interpretation techniques. The key contribution of this research focuses on atypical cases, specifically on atypical glandular cells (AGC). The expert system consists of 3 phases: (1) risk diagnosis which consists of the interpretation of a patient's clinical background and the risks for contracting CN according to specialists; (2) cytology images detection which consists of image interpretation (IM) and the Bethesda system for cytology interpretation, and (3) determination of cancer precursor injuries which consists of in retrieving the information from the prior phases and integrating the expert system by means of a fuzzy logics (FL) model. During the validation stage of the system, 21 already diagnosed cases were tested with a positive correlation in which 100% effectiveness was obtained. The main contribution of this work relies on the reduction of false positives and false negatives by providing a more accurate diagnosis for CN. PMID:23690881

  15. Breast cancer detection in rotational thermography images using texture features

    NASA Astrophysics Data System (ADS)

    Francis, Sheeja V.; Sasikala, M.; Bhavani Bharathi, G.; Jaipurkar, Sandeep D.

    2014-11-01

    Breast cancer is a major cause of mortality in young women in the developing countries. Early diagnosis is the key to improve survival rate in cancer patients. Breast thermography is a diagnostic procedure that non-invasively images the infrared emissions from breast surface to aid in the early detection of breast cancer. Due to limitations in imaging protocol, abnormality detection by conventional breast thermography, is often a challenging task. Rotational thermography is a novel technique developed in order to overcome the limitations of conventional breast thermography. This paper evaluates this technique's potential for automatic detection of breast abnormality, from the perspective of cold challenge. Texture features are extracted in the spatial domain, from rotational thermogram series, prior to and post the application of cold challenge. These features are fed to a support vector machine for automatic classification of normal and malignant breasts, resulting in a classification accuracy of 83.3%. Feature reduction has been performed by principal component analysis. As a novel attempt, the ability of this technique to locate the abnormality has been studied. The results of the study indicate that rotational thermography holds great potential as a screening tool for breast cancer detection.

  16. Feature detection on 3D images of dental imprints

    NASA Astrophysics Data System (ADS)

    Mokhtari, Marielle; Laurendeau, Denis

    1994-09-01

    A computer vision approach for the extraction of feature points on 3D images of dental imprints is presented. The position of feature points are needed for the measurement of a set of parameters for automatic diagnosis of malocclusion problems in orthodontics. The system for the acquisition of the 3D profile of the imprint, the procedure for the detection of the interstices between teeth, and the approach for the identification of the type of tooth are described, as well as the algorithm for the reconstruction of the surface of each type of tooth. A new approach for the detection of feature points, called the watershed algorithm, is described in detail. The algorithm is a two-stage procedure which tracks the position of local minima at four different scales and produces a final map of the position of the minima. Experimental results of the application of the watershed algorithm on actual 3D images of dental imprints are presented for molars, premolars and canines. The segmentation approach for the analysis of the shape of incisors is also described in detail.

  17. Collaborative Tracking of Image Features Based on Projective Invariance

    NASA Astrophysics Data System (ADS)

    Jiang, Jinwei

    -mode sensors for improving the flexibility and robustness of the system. From the experimental results during three field tests for the LASOIS system, we observed that most of the errors in the image processing algorithm are caused by the incorrect feature tracking. This dissertation addresses the feature tracking problem in image sequences acquired from cameras. Despite many alternatives to feature tracking problem, iterative least squares solution solving the optical flow equation has been the most popular approach used by many in the field. This dissertation attempts to leverage the former efforts to enhance feature tracking methods by introducing a view geometric constraint to the tracking problem, which provides collaboration among features. In contrast to alternative geometry based methods, the proposed approach provides an online solution to optical flow estimation in a collaborative fashion by exploiting Horn and Schunck flow estimation regularized by view geometric constraints. Proposed collaborative tracker estimates the motion of a feature based on the geometry of the scene and how the other features are moving. Alternative to this approach, a new closed form solution to tracking that combines the image appearance with the view geometry is also introduced. We particularly use invariants in the projective coordinates and conjecture that the traditional appearance solution can be significantly improved using view geometry. The geometric constraint is introduced by defining a new optical flow equation which exploits the scene geometry from a set drawn from tracked features. At the end of each tracking loop the quality of the tracked features is judged using both appearance similarity and geometric consistency. Our experiments demonstrate robust tracking performance even when the features are occluded or they undergo appearance changes due to projective deformation of the template. The proposed collaborative tracking method is also tested in the visual navigation

  18. Dicoogle, a Pacs Featuring Profiled Content Based Image Retrieval

    PubMed Central

    Valente, Frederico; Costa, Carlos; Silva, Augusto

    2013-01-01

    Content-based image retrieval (CBIR) has been heralded as a mechanism to cope with the increasingly larger volumes of information present in medical imaging repositories. However, generic, extensible CBIR frameworks that work natively with Picture Archive and Communication Systems (PACS) are scarce. In this article we propose a methodology for parametric CBIR based on similarity profiles. The architecture and implementation of a profiled CBIR system, based on query by example, atop Dicoogle, an open-source, full-fletched PACS is also presented and discussed. In this solution, CBIR profiles allow the specification of both a distance function to be applied and the feature set that must be present for that function to operate. The presented framework provides the basis for a CBIR expansion mechanism and the solution developed integrates with DICOM based PACS networks where it provides CBIR functionality in a seamless manner. PMID:23671578

  19. Wavelength calibration of imaging spectrometer using atmospheric absorption features

    NASA Astrophysics Data System (ADS)

    Zhou, Jiankang; Chen, Yuheng; Chen, Xinhua; Ji, Yiqun; Shen, Weimin

    2012-11-01

    Imaging spectrometer is a promising remote sensing instrument widely used in many filed, such as hazard forecasting, environmental monitoring and so on. The reliability of the spectral data is the determination to the scientific communities. The wavelength position at the focal plane of the imaging spectrometer will change as the pressure and temperature vary, or the mechanical vibration. It is difficult for the onboard calibration instrument itself to keep the spectrum reference accuracy and it also occupies weight and the volume of the remote sensing platform. Because the spectral images suffer from the atmospheric effects, the carbon oxide, water vapor, oxygen and solar Fraunhofer line, the onboard wavelength calibration can be processed by the spectral images themselves. In this paper, wavelength calibration is based on the modeled and measured atmospheric absorption spectra. The modeled spectra constructed by the atmospheric radiative transfer code. The spectral angle is used to determine the best spectral similarity between the modeled spectra and measured spectra and estimates the wavelength position. The smile shape can be obtained when the matching process across all columns of the data. The present method is successful applied on the Hyperion data. The value of the wavelength shift is obtained by shape matching of oxygen absorption feature and the characteristics are comparable to that of the prelaunch measurements.

  20. Feature identification for image-guided transcatheter aortic valve implantation

    NASA Astrophysics Data System (ADS)

    Lang, Pencilla; Rajchl, Martin; McLeod, A. Jonathan; Chu, Michael W.; Peters, Terry M.

    2012-02-01

    Transcatheter aortic valve implantation (TAVI) is a less invasive alternative to open-heart surgery, and is critically dependent on imaging for accurate placement of the new valve. Augmented image-guidance for TAVI can be provided by registering together intra-operative transesophageal echo (TEE) ultrasound and a model derived from pre-operative CT. Automatic contour delineation on TEE images of the aortic root is required for real-time registration. This study develops an algorithm to automatically extract contours on simultaneous cross-plane short-axis and long-axis (XPlane) TEE views, and register these features to a 3D pre-operative model. A continuous max-flow approach is used to segment the aortic root, followed by analysis of curvature to select appropriate contours for use in registration. Results demonstrate a mean contour boundary distance error of 1.3 and 2.8mm for the short and long-axis views respectively, and a mean target registration error of 5.9mm. Real-time image guidance has the potential to increase accuracy and reduce complications in TAVI.

  1. Morphological and texture features for cancer tissues microscopic images

    NASA Astrophysics Data System (ADS)

    Marghani, Khaled A.; Dlay, Satnam S.; Sharif, Bayan S.; Sims, Andrew J.

    2003-05-01

    Accurate and reliable decision making in cancer prognosis can help in the planning of appropriate surgery and therapy and, in general, optimize patient management through the different stages of the disease. In this paper, we present a novel fractal geometry algorithm as a potential method for classifying colorectal histopathological images. 102 microscopic samples of colon tissue were examined in order to identify abnormalities using a morphogical feature approach based on segmenting the image into different classes, derived from fractal dimension. The obtained mean fractal dimension (FD) for normal object tissue was 1.797+/- 0.0381 (n = 44) compared with 1.866+/-0.0262 for malignant samples (n = 58). In brief, this study was able to demonstrate the value of fractal dimension based on morphological approach in the analysis of microscopic colon cancer images. Although, the obtained results are strongly significant in the separation between normal and malignant colorectal images, further analyses are essential to incorporate this methodology into routine clinical practice by supporting pathologist decision.

  2. Dermatofibroma: Atypical Presentations.

    PubMed

    Bandyopadhyay, Mousumi Roy; Besra, Mrinal; Dutta, Somasree; Sarkar, Somnath

    2016-01-01

    Dermatofibroma is a common benign fibrohistiocytic tumor and its diagnosis is easy when it presents classical clinicopathological features. However, a dermatofibroma may show a wide variety of clinicopathological variants and, therefore, the diagnosis may be difficult. The typical dermatofibroma generally occurs as a single or multiple firm reddish-brown nodules. We report here two atypical presentations of dermatofibroma - Atrophic dermatofibroma and keloidal presentation of dermatofibroma. Clinical dermal atrophy is a common phenomenon in dermatofibromas as demonstrated by the dimpling on lateral pressure. However, this feature is exaggerated in the atrophic variant of dermatofibroma. Atrophic dermatofibroma is defined by dermal atrophy of more than 50% of the lesion apart from the usual features of common dermatofibroma. The keloidal variant of dermatofibroma should not be overlooked as a simple keloid. The findings of keloidal change in dermatofibromas may support that trauma is a possible cause of dermatofibroma. PMID:26955137

  3. Texture features analysis for coastline extraction in remotely sensed images

    NASA Astrophysics Data System (ADS)

    De Laurentiis, Raimondo; Dellepiane, Silvana G.; Bo, Giancarlo

    2002-01-01

    The accurate knowledge of the shoreline position is of fundamental importance in several applications such as cartography and ships positioning1. Moreover, the coastline could be seen as a relevant parameter for the monitoring of the coastal zone morphology, as it allows the retrieval of a much more precise digital elevation model of the entire coastal area. The study that has been carried out focuses on the development of a reliable technique for the detection of coastlines in remotely sensed images. An innovative approach which is based on the concepts of fuzzy connectivity and texture features extraction has been developed for the location of the shoreline. The system has been tested on several kind of images as SPOT, LANDSAT and the results obtained are good. Moreover, the algorithm has been tested on a sample of a SAR interferogram. The breakthrough consists in the fact that the coastline detection is seen as an important features in the framework of digital elevation model (DEM) retrieval. In particular, the coast could be seen as a boundary line all data beyond which (the ones representing the sea) are not significant. The processing for the digital elevation model could be refined, just considering the in-land data.

  4. Iterative feature refinement for accurate undersampled MR image reconstruction

    NASA Astrophysics Data System (ADS)

    Wang, Shanshan; Liu, Jianbo; Liu, Qiegen; Ying, Leslie; Liu, Xin; Zheng, Hairong; Liang, Dong

    2016-05-01

    Accelerating MR scan is of great significance for clinical, research and advanced applications, and one main effort to achieve this is the utilization of compressed sensing (CS) theory. Nevertheless, the existing CSMRI approaches still have limitations such as fine structure loss or high computational complexity. This paper proposes a novel iterative feature refinement (IFR) module for accurate MR image reconstruction from undersampled K-space data. Integrating IFR with CSMRI which is equipped with fixed transforms, we develop an IFR-CS method to restore meaningful structures and details that are originally discarded without introducing too much additional complexity. Specifically, the proposed IFR-CS is realized with three iterative steps, namely sparsity-promoting denoising, feature refinement and Tikhonov regularization. Experimental results on both simulated and in vivo MR datasets have shown that the proposed module has a strong capability to capture image details, and that IFR-CS is comparable and even superior to other state-of-the-art reconstruction approaches.

  5. Robust image analysis with sparse representation on quantized visual features.

    PubMed

    Bao, Bing-Kun; Zhu, Guangyu; Shen, Jialie; Yan, Shuicheng

    2013-03-01

    Recent techniques based on sparse representation (SR) have demonstrated promising performance in high-level visual recognition, exemplified by the highly accurate face recognition under occlusion and other sparse corruptions. Most research in this area has focused on classification algorithms using raw image pixels, and very few have been proposed to utilize the quantized visual features, such as the popular bag-of-words feature abstraction. In such cases, besides the inherent quantization errors, ambiguity associated with visual word assignment and misdetection of feature points, due to factors such as visual occlusions and noises, constitutes the major cause of dense corruptions of the quantized representation. The dense corruptions can jeopardize the decision process by distorting the patterns of the sparse reconstruction coefficients. In this paper, we aim to eliminate the corruptions and achieve robust image analysis with SR. Toward this goal, we introduce two transfer processes (ambiguity transfer and mis-detection transfer) to account for the two major sources of corruption as discussed. By reasonably assuming the rarity of the two kinds of distortion processes, we augment the original SR-based reconstruction objective with l(0) norm regularization on the transfer terms to encourage sparsity and, hence, discourage dense distortion/transfer. Computationally, we relax the nonconvex l(0) norm optimization into a convex l(1) norm optimization problem, and employ the accelerated proximal gradient method to optimize the convergence provable updating procedure. Extensive experiments on four benchmark datasets, Caltech-101, Caltech-256, Corel-5k, and CMU pose, illumination, and expression, manifest the necessity of removing the quantization corruptions and the various advantages of the proposed framework.

  6. Imaging and Clinicopathologic Features of Esophageal Gastrointestinal Stromal Tumors

    PubMed Central

    Winant, Abbey J.; Gollub, Marc J.; Shia, Jinru; Antonescu, Christina; Bains, Manjit S.; Levine, Marc S.

    2016-01-01

    OBJECTIVE The purpose of this article is to describe the imaging and clinicopathologic characteristics of esophageal gastrointestinal stromal tumors (GISTs) and to emphasize the features that differentiate esophageal GISTs from esophageal leiomyomas. MATERIALS AND METHODS A pathology database search identified all surgically resected or biopsied esophageal GISTs, esophageal leiomyomas, and esophageal leiomyosarcomas from 1994 to 2012. Esophageal GISTs were included only if imaging studies (including CT, fluoroscopic, or 18F-FDG PET/CT scans) and clinical data were available. RESULTS Nineteen esophageal mesenchymal tumors were identified, including eight esophageal GISTs (42%), 10 esophageal leiomyomas (53%), and one esophageal leiomyosarcoma (5%). Four patients (50%) with esophageal GIST had symptoms, including dysphagia in three (38%), cough in one (13%), and chest pain in one (13%). One esophageal GIST appeared on barium study as a smooth submucosal mass. All esophageal GISTs appeared on CT as well-marginated predominantly distal lesions, isoattenuating to muscle, that moderately enhanced after IV contrast agent administration. Compared with esophageal leiomyomas, esophageal GISTs tended to be more distal, larger, and more heterogeneous and showed greater IV enhancement on CT. All esophageal GISTs showed marked avidity (mean maximum standardized uptake value, 16) on PET scans. All esophageal GISTs were positive for c-KIT (a cell-surface transmembrane tyrosine kinase also known as CD117) and CD34. On histopathology, six esophageal GISTs (75%) were of the spindle pattern and two (25%) were of a mixed spindle and epithelioid pattern. Five esophageal GISTs had exon 11 mutations (with imatinib sensitivity). Clinical outcome correlated with treatment strategy (resection plus adjuvant therapy or resection alone) rather than risk stratification. CONCLUSION Esophageal GISTs are unusual but clinically important mesenchymal neoplasms. Although esophageal GISTs and

  7. Extraction of text-related features for condensing image documents

    NASA Astrophysics Data System (ADS)

    Bloomberg, Dan S.; Chen, Francine R.

    1996-03-01

    A system has been built that selects excerpts from a scanned document for presentation as a summary, without using character recognition. The method relies on the idea that the most significant sentences in a document contain words that are both specific to the document and have a relatively high frequency of occurrence within it. Accordingly, and entirely within the image domain, each page image is deskewed and the text regions of are found and extracted as a set of textblocks. Blocks with font size near the median for the document are selected and then placed in reading order. The textlines and words are segmented, and the words are placed into equivalence classes of similar shape. The sentences are identified by finding baselines for each line of text and analyzing the size and location of the connected components relative to the baseline. Scores can then be given to each word, depending on its shape and frequency of occurrence, and to each sentence, depending on the scores for the words in the sentence. Other salient features, such as textblocks that have a large font or are likely to contain an abstract, can also be used to select image parts that are likely to be thematically relevant. The method has been applied to a variety of documents, including articles scanned from magazines and technical journals.

  8. Synergistic combination of clinical and imaging features predicts abnormal imaging patterns of pulmonary infections

    PubMed Central

    Bagci, Ulas; Jaster-Miller, Kirsten; Olivier, Kenneth N.; Yao, Jianhua; Mollura, Daniel J.

    2013-01-01

    We designed and tested a novel hybrid statistical model that accepts radiologic image features and clinical variables, and integrates this information in order to automatically predict abnormalities in chest computed-tomography (CT) scans and identify potentially important infectious disease biomarkers. In 200 patients, 160 with various pulmonary infections and 40 healthy controls, we extracted 34 clinical variables from laboratory tests and 25 textural features from CT images. From the CT scans, pleural effusion (PE), linear opacity (or thickening) (LT), tree-in-bud (TIB), pulmonary nodules, ground glass opacity (GGO), and consolidation abnormality patterns were analyzed and predicted through clinical, textural (imaging), or combined attributes. The presence and severity of each abnormality pattern was validated by visual analysis of the CT scans. The proposed biomarker identification system included two important steps: (i) a coarse identification of an abnormal imaging pattern by adaptively selected features (AmRMR), and (ii) a fine selection of the most important features from the previous step, and assigning them as biomarkers, depending on the prediction accuracy. Selected biomarkers were used to classify normal and abnormal patterns by using a boosted decision tree (BDT) classifier. For all abnormal imaging patterns, an average prediction accuracy of 76.15% was obtained. Experimental results demonstrated that our proposed biomarker identification approach is promising and may advance the data processing in clinical pulmonary infection research and diagnostic techniques. PMID:23930819

  9. SU-E-J-237: Image Feature Based DRR and Portal Image Registration

    SciTech Connect

    Wang, X; Chang, J

    2014-06-01

    Purpose: Two-dimensional (2D) matching of the kV X-ray and digitally reconstructed radiography (DRR) images is an important setup technique for image-guided radiotherapy (IGRT). In our clinics, mutual information based methods are used for this purpose on commercial linear accelerators, but with often needs for manual corrections. This work proved the feasibility that feature based image transform can be used to register kV and DRR images. Methods: The scale invariant feature transform (SIFT) method was implemented to detect the matching image details (or key points) between the kV and DRR images. These key points represent high image intensity gradients, and thus the scale invariant features. Due to the poor image contrast from our kV image, direct application of the SIFT method yielded many detection errors. To assist the finding of key points, the center coordinates of the kV and DRR images were read from the DICOM header, and the two groups of key points with similar relative positions to their corresponding centers were paired up. Using these points, a rigid transform (with scaling, horizontal and vertical shifts) was estimated. We also artificially introduced vertical and horizontal shifts to test the accuracy of our registration method on anterior-posterior (AP) and lateral pelvic images. Results: The results provided a satisfactory overlay of the transformed kV onto the DRR image. The introduced vs. detected shifts were fit into a linear regression. In the AP image experiments, linear regression analysis showed a slope of 1.15 and 0.98 with an R2 of 0.89 and 0.99 for the horizontal and vertical shifts, respectively. The results are 1.2 and 1.3 with R2 of 0.72 and 0.82 for the lateral image shifts. Conclusion: This work provided an alternative technique for kV to DRR alignment. Further improvements in the estimation accuracy and image contrast tolerance are underway.

  10. Imaging trace element distributions in single organelles and subcellular features

    PubMed Central

    Kashiv, Yoav; Austin, Jotham R.; Lai, Barry; Rose, Volker; Vogt, Stefan; El-Muayed, Malek

    2016-01-01

    The distributions of chemical elements within cells are of prime importance in a wide range of basic and applied biochemical research. An example is the role of the subcellular Zn distribution in Zn homeostasis in insulin producing pancreatic beta cells and the development of type 2 diabetes mellitus. We combined transmission electron microscopy with micro- and nano-synchrotron X-ray fluorescence to image unequivocally for the first time, to the best of our knowledge, the natural elemental distributions, including those of trace elements, in single organelles and other subcellular features. Detected elements include Cl, K, Ca, Co, Ni, Cu, Zn and Cd (which some cells were supplemented with). Cell samples were prepared by a technique that minimally affects the natural elemental concentrations and distributions, and without using fluorescent indicators. It could likely be applied to all cell types and provide new biochemical insights at the single organelle level not available from organelle population level studies. PMID:26911251

  11. Imaging trace element distributions in single organelles and subcellular features

    NASA Astrophysics Data System (ADS)

    Kashiv, Yoav; Austin, Jotham R.; Lai, Barry; Rose, Volker; Vogt, Stefan; El-Muayed, Malek

    2016-02-01

    The distributions of chemical elements within cells are of prime importance in a wide range of basic and applied biochemical research. An example is the role of the subcellular Zn distribution in Zn homeostasis in insulin producing pancreatic beta cells and the development of type 2 diabetes mellitus. We combined transmission electron microscopy with micro- and nano-synchrotron X-ray fluorescence to image unequivocally for the first time, to the best of our knowledge, the natural elemental distributions, including those of trace elements, in single organelles and other subcellular features. Detected elements include Cl, K, Ca, Co, Ni, Cu, Zn and Cd (which some cells were supplemented with). Cell samples were prepared by a technique that minimally affects the natural elemental concentrations and distributions, and without using fluorescent indicators. It could likely be applied to all cell types and provide new biochemical insights at the single organelle level not available from organelle population level studies.

  12. Combining image features, case descriptions and UMLS concepts to improve retrieval of medical images.

    PubMed

    Ruiz, Miguel E

    2006-01-01

    This paper evaluates a system, UBMedTIRS, for retrieval of medical images. The system uses a combination of image and text features as well as mapping of free text to UMLS concepts. UBMedTIRS combines three publicly available tools: a content-based image retrieval system (GIFT), a text retrieval system (SMART), and a tool for mapping free text to UMLS concepts (MetaMap). The system is evaluated using the ImageCLEFmed 2005 collection that contains approximately 50,000 medical images with associated text descriptions in English, French and German. Our experimental results indicate that the proposed approach yields significant improvements in retrieval performance. Our system performs 156% above the GIFT system and 42% above the text retrieval system.

  13. Hemicrania continua: functional imaging and clinical features with diagnostic implications.

    PubMed

    Sahler, Kristen

    2013-05-01

    This review focuses on summarizing 2 pivotal articles in the clinical and pathophysiologic understanding of hemicrania continua (HC). The first article, a functional imaging project,identifies both the dorsal rostral pons (a region associated with the generation of migraines) and the posterior hypothalamus(a region associated with the generation of cluster and short-lasting unilateral neuralgiform headache with conjunctival injection and tearing [SUNCT]) as active during HC. The second article is a summary of the clinical features seen in a prospective cohort of HC patients that carry significant diagnostic implications. In particular, they identify a wider range of autonomic signs than what is currently included in the International Headache Society criteria (including an absence of autonomic signs in a small percentage of patients), a high frequency of migrainous features, and the presence of aggravation and/or restlessness during attacks. Wide variations in exacerbation length, frequency, pain description, and pain location (including side-switching pain) are also noted. Thus, a case is made for widening and modifying the clinical diagnostic criteria used to identify patients with HC.

  14. Hemicrania Continua: Functional Imaging and Clinical Features With Diagnostic Implications.

    PubMed

    Sahler, Kristen

    2013-04-10

    This review focuses on summarizing 2 pivotal articles in the clinical and pathophysiologic understanding of hemicrania continua (HC). The first article, a functional imaging project, identifies both the dorsal rostral pons (a region associated with the generation of migraines) and the posterior hypothalamus (a region associated with the generation of cluster and short-lasting unilateral neuralgiform headache with conjunctival injection and tearing [SUNCT]) as active during HC. The second article is a summary of the clinical features seen in a prospective cohort of HC patients that carry significant diagnostic implications. In particular, they identify a wider range of autonomic signs than what is currently included in the International Headache Society criteria (including an absence of autonomic signs in a small percentage of patients), a high frequency of migrainous features, and the presence of aggravation and/or restlessness during attacks. Wide variations in exacerbation length, frequency, pain description, and pain location (including side-switching pain) are also noted. Thus, a case is made for widening and modifying the clinical diagnostic criteria used to identify patients with HC.

  15. Covert photo classification by fusing image features and visual attributes.

    PubMed

    Lang, Haitao; Ling, Haibin

    2015-10-01

    In this paper, we study a novel problem of classifying covert photos, whose acquisition processes are intentionally concealed from the subjects being photographed. Covert photos are often privacy invasive and, if distributed over Internet, can cause serious consequences. Automatic identification of such photos, therefore, serves as an important initial step toward further privacy protection operations. The problem is, however, very challenging due to the large semantic similarity between covert and noncovert photos, the enormous diversity in the photographing process and environment of cover photos, and the difficulty to collect an effective data set for the study. Attacking these challenges, we make three consecutive contributions. First, we collect a large data set containing 2500 covert photos, each of them is verified rigorously and carefully. Second, we conduct a user study on how humans distinguish covert photos from noncovert ones. The user study not only provides an important evaluation baseline, but also suggests fusing heterogeneous information for an automatic solution. Our third contribution is a covert photo classification algorithm that fuses various image features and visual attributes in the multiple kernel learning framework. We evaluate the proposed approach on the collected data set in comparison with other modern image classifiers. The results show that our approach achieves an average classification rate (1-EER) of 0.8940, which significantly outperforms other competitors as well as human's performance.

  16. Feature Extraction in Sequential Multimedia Images: with Applications in Satellite Images and On-line Videos

    NASA Astrophysics Data System (ADS)

    Liang, Yu-Li

    Multimedia data is increasingly important in scientific discovery and people's daily lives. Content of massive multimedia is often diverse and noisy, and motion between frames is sometimes crucial in analyzing those data. Among all, still images and videos are commonly used formats. Images are compact in size but do not contain motion information. Videos record motion but are sometimes too big to be analyzed. Sequential images, which are a set of continuous images with low frame rate, stand out because they are smaller than videos and still maintain motion information. This thesis investigates features in different types of noisy sequential images, and the proposed solutions that intelligently combined multiple features to successfully retrieve visual information from on-line videos and cloudy satellite images. The first task is detecting supraglacial lakes above ice sheet in sequential satellite images. The dynamics of supraglacial lakes on the Greenland ice sheet deeply affect glacier movement, which is directly related to sea level rise and global environment change. Detecting lakes above ice is suffering from diverse image qualities and unexpected clouds. A new method is proposed to efficiently extract prominent lake candidates with irregular shapes, heterogeneous backgrounds, and in cloudy images. The proposed system fully automatize the procedure that track lakes with high accuracy. We further cooperated with geoscientists to examine the tracked lakes and found new scientific findings. The second one is detecting obscene content in on-line video chat services, such as Chatroulette, that randomly match pairs of users in video chat sessions. A big problem encountered in such systems is the presence of flashers and obscene content. Because of various obscene content and unstable qualities of videos capture by home web-camera, detecting misbehaving users is a highly challenging task. We propose SafeVchat, which is the first solution that achieves satisfactory

  17. Application of image visual characterization and soft feature selection in content-based image retrieval

    NASA Astrophysics Data System (ADS)

    Jarrah, Kambiz; Kyan, Matthew; Lee, Ivan; Guan, Ling

    2006-01-01

    Fourier descriptors (FFT) and Hu's seven moment invariants (HSMI) are among the most popular shape-based image descriptors and have been used in various applications, such as recognition, indexing, and retrieval. In this work, we propose to use the invariance properties of Hu's seven moment invariants, as shape feature descriptors, for relevance identification in content-based image retrieval (CBIR) systems. The purpose of relevance identification is to find a collection of images that are statistically similar to, or match with, an original query image from within a large visual database. An automatic relevance identification module in the search engine is structured around an unsupervised learning algorithm, the self-organizing tree map (SOTM). In this paper we also proposed a new ranking function in the structure of the SOTM that exponentially ranks the retrieved images based on their similarities with respect to the query image. Furthermore, we propose to extend our studies to optimize the contribution of individual feature descriptors for enhancing the retrieval results. The proposed CBIR system is compatible with the different architectures of other CBIR systems in terms of its ability to adapt to different similarity matching algorithms for relevance identification purposes, whilst offering flexibility of choice for alternative optimization and weight estimation techniques. Experimental results demonstrate the satisfactory performance of the proposed CBIR system.

  18. Image sensor innovations for low light levels with active imaging features

    NASA Astrophysics Data System (ADS)

    Powell, Gareth H.; Fereyre, Pierre

    2015-02-01

    Advances in CMOS imaging enable image capture at lower light levels. Color detection is also possible where human vision becomes less sensitive in night conditions. In daytime conditions, there are a number of climatic conditions such as rain, fog, snow or smoke etc. that render traditional `intelligent' outdoor cameras that perform various forms of detection and identification tasks relatively ineffective. It has been proven that an adapted five transistor pixel CMOS sensor can perform range-gated active imaging that extends considerably the usability of intelligent cameras in the most difficult conditions. This paper discusses advanced state of the art image sensors with embedded features, with emphasis on the everimportant size, weight, power and cost benefits and discusses the new applications that are enabled.

  19. Scalable High Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning

    PubMed Central

    Wu, Guorong; Kim, Minjeong; Wang, Qian; Munsell, Brent C.

    2015-01-01

    Feature selection is a critical step in deformable image registration. In particular, selecting the most discriminative features that accurately and concisely describe complex morphological patterns in image patches improves correspondence detection, which in turn improves image registration accuracy. Furthermore, since more and more imaging modalities are being invented to better identify morphological changes in medical imaging data,, the development of deformable image registration method that scales well to new image modalities or new image applications with little to no human intervention would have a significant impact on the medical image analysis community. To address these concerns, a learning-based image registration framework is proposed that uses deep learning to discover compact and highly discriminative features upon observed imaging data. Specifically, the proposed feature selection method uses a convolutional stacked auto-encoder to identify intrinsic deep feature representations in image patches. Since deep learning is an unsupervised learning method, no ground truth label knowledge is required. This makes the proposed feature selection method more flexible to new imaging modalities since feature representations can be directly learned from the observed imaging data in a very short amount of time. Using the LONI and ADNI imaging datasets, image registration performance was compared to two existing state-of-the-art deformable image registration methods that use handcrafted features. To demonstrate the scalability of the proposed image registration framework image registration experiments were conducted on 7.0-tesla brain MR images. In all experiments, the results showed the new image registration framework consistently demonstrated more accurate registration results when compared to state-of-the-art. PMID:26552069

  20. Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning.

    PubMed

    Wu, Guorong; Kim, Minjeong; Wang, Qian; Munsell, Brent C; Shen, Dinggang

    2016-07-01

    Feature selection is a critical step in deformable image registration. In particular, selecting the most discriminative features that accurately and concisely describe complex morphological patterns in image patches improves correspondence detection, which in turn improves image registration accuracy. Furthermore, since more and more imaging modalities are being invented to better identify morphological changes in medical imaging data, the development of deformable image registration method that scales well to new image modalities or new image applications with little to no human intervention would have a significant impact on the medical image analysis community. To address these concerns, a learning-based image registration framework is proposed that uses deep learning to discover compact and highly discriminative features upon observed imaging data. Specifically, the proposed feature selection method uses a convolutional stacked autoencoder to identify intrinsic deep feature representations in image patches. Since deep learning is an unsupervised learning method, no ground truth label knowledge is required. This makes the proposed feature selection method more flexible to new imaging modalities since feature representations can be directly learned from the observed imaging data in a very short amount of time. Using the LONI and ADNI imaging datasets, image registration performance was compared to two existing state-of-the-art deformable image registration methods that use handcrafted features. To demonstrate the scalability of the proposed image registration framework, image registration experiments were conducted on 7.0-T brain MR images. In all experiments, the results showed that the new image registration framework consistently demonstrated more accurate registration results when compared to state of the art. PMID:26552069

  1. Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning.

    PubMed

    Wu, Guorong; Kim, Minjeong; Wang, Qian; Munsell, Brent C; Shen, Dinggang

    2016-07-01

    Feature selection is a critical step in deformable image registration. In particular, selecting the most discriminative features that accurately and concisely describe complex morphological patterns in image patches improves correspondence detection, which in turn improves image registration accuracy. Furthermore, since more and more imaging modalities are being invented to better identify morphological changes in medical imaging data, the development of deformable image registration method that scales well to new image modalities or new image applications with little to no human intervention would have a significant impact on the medical image analysis community. To address these concerns, a learning-based image registration framework is proposed that uses deep learning to discover compact and highly discriminative features upon observed imaging data. Specifically, the proposed feature selection method uses a convolutional stacked autoencoder to identify intrinsic deep feature representations in image patches. Since deep learning is an unsupervised learning method, no ground truth label knowledge is required. This makes the proposed feature selection method more flexible to new imaging modalities since feature representations can be directly learned from the observed imaging data in a very short amount of time. Using the LONI and ADNI imaging datasets, image registration performance was compared to two existing state-of-the-art deformable image registration methods that use handcrafted features. To demonstrate the scalability of the proposed image registration framework, image registration experiments were conducted on 7.0-T brain MR images. In all experiments, the results showed that the new image registration framework consistently demonstrated more accurate registration results when compared to state of the art.

  2. A combinatorial Bayesian and Dirichlet model for prostate MR image segmentation using probabilistic image features

    NASA Astrophysics Data System (ADS)

    Li, Ang; Li, Changyang; Wang, Xiuying; Eberl, Stefan; Feng, Dagan; Fulham, Michael

    2016-08-01

    Blurred boundaries and heterogeneous intensities make accurate prostate MR image segmentation problematic. To improve prostate MR image segmentation we suggest an approach that includes: (a) an image patch division method to partition the prostate into homogeneous segments for feature extraction; (b) an image feature formulation and classification method, using the relevance vector machine, to provide probabilistic prior knowledge for graph energy construction; (c) a graph energy formulation scheme with Bayesian priors and Dirichlet graph energy and (d) a non-iterative graph energy minimization scheme, based on matrix differentiation, to perform the probabilistic pixel membership optimization. The segmentation output was obtained by assigning pixels with foreground and background labels based on derived membership probabilities. We evaluated our approach on the PROMISE-12 dataset with 50 prostate MR image volumes. Our approach achieved a mean dice similarity coefficient (DSC) of 0.90  ±  0.02, which surpassed the five best prior-based methods in the PROMISE-12 segmentation challenge.

  3. A combinatorial Bayesian and Dirichlet model for prostate MR image segmentation using probabilistic image features.

    PubMed

    Li, Ang; Li, Changyang; Wang, Xiuying; Eberl, Stefan; Feng, Dagan; Fulham, Michael

    2016-08-21

    Blurred boundaries and heterogeneous intensities make accurate prostate MR image segmentation problematic. To improve prostate MR image segmentation we suggest an approach that includes: (a) an image patch division method to partition the prostate into homogeneous segments for feature extraction; (b) an image feature formulation and classification method, using the relevance vector machine, to provide probabilistic prior knowledge for graph energy construction; (c) a graph energy formulation scheme with Bayesian priors and Dirichlet graph energy and (d) a non-iterative graph energy minimization scheme, based on matrix differentiation, to perform the probabilistic pixel membership optimization. The segmentation output was obtained by assigning pixels with foreground and background labels based on derived membership probabilities. We evaluated our approach on the PROMISE-12 dataset with 50 prostate MR image volumes. Our approach achieved a mean dice similarity coefficient (DSC) of 0.90  ±  0.02, which surpassed the five best prior-based methods in the PROMISE-12 segmentation challenge. PMID:27461085

  4. Image Labeling for LIDAR Intensity Image Using K-Nn of Feature Obtained by Convolutional Neural Network

    NASA Astrophysics Data System (ADS)

    Umemura, Masaki; Hotta, Kazuhiro; Nonaka, Hideki; Oda, Kazuo

    2016-06-01

    We propose an image labeling method for LIDAR intensity image obtained by Mobile Mapping System (MMS) using K-Nearest Neighbor (KNN) of feature obtained by Convolutional Neural Network (CNN). Image labeling assigns labels (e.g., road, cross-walk and road shoulder) to semantic regions in an image. Since CNN is effective for various image recognition tasks, we try to use the feature of CNN (Caffenet) pre-trained by ImageNet. We use 4,096-dimensional feature at fc7 layer in the Caffenet as the descriptor of a region because the feature at fc7 layer has effective information for object classification. We extract the feature by the Caffenet from regions cropped from images. Since the similarity between features reflects the similarity of contents of regions, we can select top K similar regions cropped from training samples with a test region. Since regions in training images have manually-annotated ground truth labels, we vote the labels attached to top K similar regions to the test region. The class label with the maximum vote is assigned to each pixel in the test image. In experiments, we use 36 LIDAR intensity images with ground truth labels. We divide 36 images into training (28 images) and test sets (8 images). We use class average accuracy and pixel-wise accuracy as evaluation measures. Our method was able to assign the same label as human beings in 97.8% of the pixels in test LIDAR intensity images.

  5. An Open Source Agenda for Research Linking Text and Image Content Features.

    ERIC Educational Resources Information Center

    Goodrum, Abby A.; Rorvig, Mark E.; Jeong, Ki-Tai; Suresh, Chitturi

    2001-01-01

    Proposes methods to utilize image primitives to support term assignment for image classification. Proposes to release code for image analysis in a common tool set for other researchers to use. Of particular focus is the expansion of work by researchers in image indexing to include image content-based feature extraction capabilities in their work.…

  6. Imaging trace element distributions in single organelles and subcellular features

    DOE PAGESBeta

    Kashiv, Yoav; Austin, Jotham R.; Lai, Barry; Rose, Volker; Vogt, Stefan; El-Muayed, Malek

    2016-02-25

    The distributions of chemical elements within cells are of prime importance in a wide range of basic and applied biochemical research. An example is the role of the subcellular Zn distribution in Zn homeostasis in insulin producing pancreatic beta cells and the development of type 2 diabetes mellitus. We combined transmission electron microscopy with micro- and nano-synchrotron X-ray fluorescence to image unequivocally for the first time, to the best of our knowledge, the natural elemental distributions, including those of trace elements, in single organelles and other subcellular features. Detected elements include Cl, K, Ca, Co, Ni, Cu, Zn and Cdmore » (which some cells were supplemented with). Cell samples were prepared by a technique that minimally affects the natural elemental concentrations and distributions, and without using fluorescent indicators.We find it could likely be applied to all cell types and provide new biochemical insights at the single organelle level not available from organelle population level studies.« less

  7. Atypical moles: diagnosis and management.

    PubMed

    Perkins, Allen; Duffy, R Lamar

    2015-06-01

    Atypical moles are benign pigmented lesions. Although they are benign, they exhibit some of the clinical and histologic features of malignant melanoma. They are more common in fair-skinned individuals and in those with high sun exposure. Atypical moles are characterized by size of 6 mm or more at the greatest dimension, color variegation, border irregularity, and pebbled texture. They are associated with an increased risk of melanoma, warranting enhanced surveillance, especially in patients with more than 50 moles and a family history of melanoma. Because an individual lesion is unlikely to display malignant transformation, biopsy of all atypical moles is neither clinically beneficial nor cost-effective. The ABCDE (asymmetry, border irregularity, color unevenness, diameter of 6 mm or more, evolution) mnemonic is a valuable tool for clinicians and patients to identify lesions that could be melanoma. Also, according to the "ugly duckling" concept, benign moles tend to have a similar appearance, whereas an outlier with a different appearance is more likely to be undergoing malignant change. Atypical moles with changes suggestive of malignant melanoma should be biopsied, using an excisional method, if possible.

  8. The MAPT p.A152T variant is a risk factor associated with tauopathies with atypical clinical and neuropathological features.

    PubMed

    Kara, Eleanna; Ling, Helen; Pittman, Alan M; Shaw, Karen; de Silva, Rohan; Simone, Roberto; Holton, Janice L; Warren, Jason D; Rohrer, Jonathan D; Xiromerisiou, Georgia; Lees, Andrew; Hardy, John; Houlden, Henry; Revesz, Tamas

    2012-09-01

    Microtubule-associated protein tau (MAPT) mutations have been shown to underlie frontotemporal dementia and a variety of additional sporadic tauopathies. We identified a rare p.A152T variant in MAPT exon 7 in two (of eight) patients with clinical presentation of parkinsonism and postmortem finding of neurofibrillary tangle pathology. Two siblings of one patient also carried the p.A152T variant, and both have progressive cognitive impairment. Further screening identified the variant in two other cases: one with pathologically confirmed corticobasal degeneration and another with the diagnosis of Parkinson's disease with dementia. The balance of evidence suggests this variant is associated with disease, but the very varied phenotype of the cases with the mutation is not consistent with it being a fully penetrant pathogenic mutation. Interestingly, this variation results in the creation of a new phosphorylation site that could cause reduced microtubule binding. We suggest that the A152T variant is a risk factor associated with the development of atypical neurodegenerative conditions with abnormal tau accumulation.

  9. Hierarchical Multi-modal Image Registration by Learning Common Feature Representations

    PubMed Central

    Ge, Hongkun; Wu, Guorong; Wang, Li; Gao, Yaozong

    2016-01-01

    Mutual information (MI) has been widely used for registering images with different modalities. Since most inter-modality registration methods simply estimate deformations in a local scale, but optimizing MI from the entire image, the estimated deformations for certain structures could be dominated by the surrounding unrelated structures. Also, since there often exist multiple structures in each image, the intensity correlation between two images could be complex and highly nonlinear, which makes global MI unable to precisely guide local image deformation. To solve these issues, we propose a hierarchical inter-modality registration method by robust feature matching. Specifically, we first select a small set of key points at salient image locations to drive the entire image registration. Since the original image features computed from different modalities are often difficult for direct comparison, we propose to learn their common feature representations by projecting them from their native feature spaces to a common space, where the correlations between corresponding features are maximized. Due to the large heterogeneity between two high-dimension feature distributions, we employ Kernel CCA (Canonical Correlation Analysis) to reveal such non-linear feature mappings. Then, our registration method can take advantage of the learned common features to reliably establish correspondences for key points from different modality images by robust feature matching. As more and more key points take part in the registration, our hierarchical feature-based image registration method can efficiently estimate the deformation pathway between two inter-modality images in a global to local manner. We have applied our proposed registration method to prostate CT and MR images, as well as the infant MR brain images in the first year of life. Experimental results show that our method can achieve more accurate registration results, compared to other state-of-the-art image registration

  10. Image registration algorithm using Mexican hat function-based operator and grouped feature matching strategy.

    PubMed

    Jin, Feng; Feng, Dazheng

    2014-01-01

    Feature detection and matching are crucial for robust and reliable image registration. Although many methods have been developed, they commonly focus on only one class of image features. The methods that combine two or more classes of features are still novel and significant. In this work, methods for feature detection and matching are proposed. A Mexican hat function-based operator is used for image feature detection, including the local area detection and the feature point detection. For the local area detection, we use the Mexican hat operator for image filtering, and then the zero-crossing points are extracted and merged into the area borders. For the feature point detection, the Mexican hat operator is performed in scale space to get the key points. After the feature detection, an image registration is achieved by using the two classes of image features. The feature points are grouped according to a standardized region that contains correspondence to the local area, precise registration is achieved eventually by the grouped points. An image transformation matrix is estimated by the feature points in a region and then the best one is chosen through competition of a set of the transformation matrices. This strategy has been named the Grouped Sample Consensus (GCS). The GCS has also ability for removing the outliers effectively. The experimental results show that the proposed algorithm has high registration accuracy and small computational volume. PMID:24752223

  11. Variability of Image Features Computed from Conventional and Respiratory-Gated PET/CT Images of Lung Cancer

    PubMed Central

    Oliver, Jasmine A.; Budzevich, Mikalai; Zhang, Geoffrey G.; Dilling, Thomas J.; Latifi, Kujtim; Moros, Eduardo G.

    2015-01-01

    Radiomics is being explored for potential applications in radiation therapy. How various imaging protocols affect quantitative image features is currently a highly active area of research. To assess the variability of image features derived from conventional [three-dimensional (3D)] and respiratory-gated (RG) positron emission tomography (PET)/computed tomography (CT) images of lung cancer patients, image features were computed from 23 lung cancer patients. Both protocols for each patient were acquired during the same imaging session. PET tumor volumes were segmented using an adaptive technique which accounted for background. CT tumor volumes were delineated with a commercial segmentation tool. Using RG PET images, the tumor center of mass motion, length, and rotation were calculated. Fifty-six image features were extracted from all images consisting of shape descriptors, first-order features, and second-order texture features. Overall, 26.6% and 26.2% of total features demonstrated less than 5% difference between 3D and RG protocols for CT and PET, respectively. Between 10 RG phases in PET, 53.4% of features demonstrated percent differences less than 5%. The features with least variability for PET were sphericity, spherical disproportion, entropy (first and second order), sum entropy, information measure of correlation 2, Short Run Emphasis (SRE), Long Run Emphasis (LRE), and Run Percentage (RPC); and those for CT were minimum intensity, mean intensity, Root Mean Square (RMS), Short Run Emphasis (SRE), and RPC. Quantitative analysis using a 3D acquisition versus RG acquisition (to reduce the effects of motion) provided notably different image feature values. This study suggests that the variability between 3D and RG features is mainly due to the impact of respiratory motion. PMID:26692535

  12. Solution Structure of 4'-Phosphopantetheine - GmACP3 from Geobacter Metallireducens: A Specialized Acyl Carrier Protein with Atypical Structural Features and a Putative Role in Lipopolysaccharide Biosyntheses

    SciTech Connect

    Ramelot, Theresa A.; Smola, Matthew J.; Lee, Hsiau-Wei; Ciccosanti, Colleen; Hamilton, Keith; Acton, Thomas; Xiao, Rong; Everett, John K.; Prestegard, James H.; Montelione, Gaetano; Kennedy, Michael A.

    2011-03-08

    GmACP3 from Geobacter metallireducens is a specialized acyl carrier protein (ACP) whose gene, gmet_2339, is located near genes encoding many proteins involved in lipopolysaccharide (LPS) biosynthesis, indicating a likely function for GmACP3 in LPS production. By overexpression in Escherichia coli, about 50% holo-GmACP3 and 50% apo-GmACP3 were obtained. Apo-GmACP3 exhibited slow precipitation and non-monomeric behavior by 15NNMRrelaxation measurements. Addition of 4'-phosphopantetheine (4'-PP) via enzymatic conversion by E. coli holo-ACP synthase resulted in stable >95% holo-GmACP3 that was characterized as monomeric by 15N relaxation measurements and had no indication of conformational exchange. We have determined a high-resolution solution structure of holo-GmACP3 by standard NMR methods, including refinement with two sets of NH residual dipolar couplings, allowing for a detailed structural analysis of the interactions between 4'-PP and GmACP3. Whereas the overall four helix bundle topology is similar to previously solved ACP structures, this structure has unique characteristics, including an ordered 4'-PP conformation that places the thiol at the entrance to a central hydrophobic cavity near a conserved hydrogen-bonded Trp-His pair. These residues are part of a conservedWDSLxH/N motif found in GmACP3 and its orthologs. The helix locations and the large hydrophobic cavity are more similar tomediumand long-chain acyl-ACPs than to other apo- and holo-ACP structures. Taken together, structural characterization along with bioinformatic analysis of nearby genes suggests that GmACP3 is involved in lipid A acylation, possibly by atypical long-chain hydroxy fatty acids, and potentially is involved in synthesis of secondary metabolites.

  13. [Research on non-rigid medical image registration algorithm based on SIFT feature extraction].

    PubMed

    Wang, Anna; Lu, Dan; Wang, Zhe; Fang, Zhizhen

    2010-08-01

    In allusion to non-rigid registration of medical images, the paper gives a practical feature points matching algorithm--the image registration algorithm based on the scale-invariant features transform (Scale Invariant Feature Transform, SIFT). The algorithm makes use of the image features of translation, rotation and affine transformation invariance in scale space to extract the image feature points. Bidirectional matching algorithm is chosen to establish the matching relations between the images, so the accuracy of image registrations is improved. On this basis, affine transform is chosen to complement the non-rigid registration, and normalized mutual information measure and PSO optimization algorithm are also chosen to optimize the registration process. The experimental results show that the method can achieve better registration results than the method based on mutual information.

  14. A comparison study of textural features between FFDM and film mammogram images

    NASA Astrophysics Data System (ADS)

    Jing, Hao; Yang, Yongyi; Wernick, Miles N.; Yarusso, Laura M.; Nishikawa, Robert M.

    2011-03-01

    In this work, we conducted an imaging study to make a direct, quantitative comparison of image features measured by film and full-field digital mammography (FFDM). We acquired images of cadaver breast specimens containing simulated microcalcifications using both a GE digital mammography system and a screen-film system. To quantify the image features, we calculated and compared a set of 12 texture features derived from spatial gray-level dependence matrices. Our results demonstrate that there is a great degree of agreement between film and FFDM, with the correlation coefficient of the feature vector (formed by the 12 textural features) being 0.9569 between the two; in addition, a paired sign test reveals no significant difference between film and FFDM features. These results indicate that textural features may be interchangeable between film and FFDM for CAD algorithms.

  15. Imaging features of intradural spinal paragonimiasis: a case report

    PubMed Central

    Kim, M K; Cho, B M; Yoon, D Y; Nam, E S

    2011-01-01

    Spinal paragonimiasis is a rare form of ectopic infestation caused by Paragonimus westermani. We report a case of pathologically proven intradural paragonimiasis associated with concurrent intracranial involvement. MRI revealed multiple well-defined intradural masses that were markedly hypointense on T2 weighted images and hypointense with a peripheral hyperintense rim on T1 weighted images. Contrast-enhanced T1 weighted images showed slight peripheral rim enhancement. PMID:21415296

  16. Some features of photolithography image formation in partially coherent light

    SciTech Connect

    Kitsak, M A; Kitsak, A I

    2010-12-09

    The coherent-noise level in projection images of an opaque-screen sharp edge, formed in the model scheme of photolithography system at different degrees of spatial coherence of screen-illuminating light is studied experimentally. The spatial coherence of laser radiation was reduced by applying a specially developed device, used as a separate functional unit in the system model. The smoothing of the spatial fluctuations of radiation intensity caused by the random spatial inhomogeneity of the initial beam intensity in the obtained images is shown to be highly efficient. (imaging and image processing. holography)

  17. Unsupervised detection of abnormalities in medical images using salient features

    NASA Astrophysics Data System (ADS)

    Alpert, Sharon; Kisilev, Pavel

    2014-03-01

    In this paper we propose a new method for abnormality detection in medical images which is based on the notion of medical saliency. The proposed method is general and is suitable for a variety of tasks related to detection of: 1) lesions and microcalcifications (MCC) in mammographic images, 2) stenoses in angiographic images, 3) lesions found in magnetic resonance (MRI) images of brain. The main idea of our approach is that abnormalities manifest as rare events, that is, as salient areas compared to normal tissues. We define the notion of medical saliency by combining local patch information from the lightness channel with geometric shape local descriptors. We demonstrate the efficacy of the proposed method by applying it to various modalities, and to various abnormality detection problems. Promising results are demonstrated for detection of MCC and of masses in mammographic images, detection of stenoses in angiography images, and detection of lesions in brain MRI. We also demonstrate how the proposed automatic abnormality detection method can be combined with a system that performs supervised classification of mammogram images into benign or malignant/premalignant MCC's. We use a well known DDSM mammogram database for the experiment on MCC classification, and obtain 80% accuracy in classifying images containing premalignant MCC versus benign ones. In contrast to supervised detection methods, the proposed approach does not rely on ground truth markings, and, as such, is very attractive and applicable for big corpus image data processing.

  18. Multiple Myeloma: A Review of Imaging Features and Radiological Techniques

    PubMed Central

    Healy, C. F.; Murray, J. G.; Eustace, S. J.; Madewell, J.; O'Gorman, P. J.; O'Sullivan, P.

    2011-01-01

    The recently updated Durie/Salmon PLUS staging system published in 2006 highlights the many advances that have been made in the imaging of multiple myeloma, a common malignancy of plasma cells. In this article, we shall focus primarily on the more sensitive and specific whole-body imaging techniques, including whole-body computed tomography, whole-body magnetic resonance imaging, and positron emission computed tomography. We shall also discuss new and emerging imaging techniques and future developments in the radiological assessment of multiple myeloma. PMID:22046568

  19. Multiple myeloma: a review of imaging features and radiological techniques.

    PubMed

    Healy, C F; Murray, J G; Eustace, S J; Madewell, J; O'Gorman, P J; O'Sullivan, P

    2011-01-01

    The recently updated Durie/Salmon PLUS staging system published in 2006 highlights the many advances that have been made in the imaging of multiple myeloma, a common malignancy of plasma cells. In this article, we shall focus primarily on the more sensitive and specific whole-body imaging techniques, including whole-body computed tomography, whole-body magnetic resonance imaging, and positron emission computed tomography. We shall also discuss new and emerging imaging techniques and future developments in the radiological assessment of multiple myeloma.

  20. Change Detection in Uav Video Mosaics Combining a Feature Based Approach and Extended Image Differencing

    NASA Astrophysics Data System (ADS)

    Saur, Günter; Krüger, Wolfgang

    2016-06-01

    Change detection is an important task when using unmanned aerial vehicles (UAV) for video surveillance. We address changes of short time scale using observations in time distances of a few hours. Each observation (previous and current) is a short video sequence acquired by UAV in near-Nadir view. Relevant changes are, e.g., recently parked or moved vehicles. Examples for non-relevant changes are parallaxes caused by 3D structures of the scene, shadow and illumination changes, and compression or transmission artifacts. In this paper we present (1) a new feature based approach to change detection, (2) a combination with extended image differencing (Saur et al., 2014), and (3) the application to video sequences using temporal filtering. In the feature based approach, information about local image features, e.g., corners, is extracted in both images. The label "new object" is generated at image points, where features occur in the current image and no or weaker features are present in the previous image. The label "vanished object" corresponds to missing or weaker features in the current image and present features in the previous image. This leads to two "directed" change masks and differs from image differencing where only one "undirected" change mask is extracted which combines both label types to the single label "changed object". The combination of both algorithms is performed by merging the change masks of both approaches. A color mask showing the different contributions is used for visual inspection by a human image interpreter.

  1. Image mosaicking based on feature points using color-invariant values

    NASA Astrophysics Data System (ADS)

    Lee, Dong-Chang; Kwon, Oh-Seol; Ko, Kyung-Woo; Lee, Ho-Young; Ha, Yeong-Ho

    2008-02-01

    In the field of computer vision, image mosaicking is achieved using image features, such as textures, colors, and shapes between corresponding images, or local descriptors representing neighborhoods of feature points extracted from corresponding images. However, image mosaicking based on feature points has attracted more recent attention due to the simplicity of the geometric transformation, regardless of distortion and differences in intensity generated by camera motion in consecutive images. Yet, since most feature-point matching algorithms extract feature points using gray values, identifying corresponding points becomes difficult in the case of changing illumination and images with a similar intensity. Accordingly, to solve these problems, this paper proposes a method of image mosaicking based on feature points using color information of images. Essentially, the digital values acquired from a real digital color camera are converted to values of a virtual camera with distinct narrow bands. Values based on the surface reflectance and invariant to the chromaticity of various illuminations are then derived from the virtual camera values and defined as color-invariant values invariant to changing illuminations. The validity of these color-invariant values is verified in a test using a Macbeth Color-Checker under simulated illuminations. The test also compares the proposed method using the color-invariant values with the conventional SIFT algorithm. The accuracy of the matching between the feature points extracted using the proposed method is increased, while image mosaicking using color information is also achieved.

  2. Electronic expert consultation using digital still images for evaluation of atypical small acinar proliferations of the prostate: a comparison with immunohistochemistry.

    PubMed

    Banihashemi, Amir; Asgari, Mojgan; Shooshtarizade, Tina; Abolhasani, Maryam; Mireskandari, Masoud

    2014-06-01

    This study was performed on a series of prostate needle biopsies with diagnosis of atypical small acinar proliferation (ASAP) to verify to what extent the application of immunohistochemistry (IHC) for p504s and p63 markers as well as expert consultation by still images could affect the diagnosis. The results of these 2 methods were compared. Immunohistochemistry staining for p504s and p63 was performed on sections from 42 patients with a primary diagnosis of ASAP. Meanwhile, digital still images were taken from hematoxylin and eosin-stained slides of cases and were sent to an expert uropathologist, blind to IHC staining interpretations. The results of IHC staining were compared with diagnostic interpretations of the consultant pathologist. In 13 cases, the focus of concern was not detectable on IHC slides. In the remaining 29 cases, IHC showed a benign and malignant expression pattern in 17 and 9 patients, respectively. In 3 cases, IHC findings were inconclusive and retained the diagnosis of ASAP. The consultant pathologist diagnosed 11 cases of benign and 7 cases of malignant processes. He retained the diagnosis of ASAP in 11 cases. There was high concordance between the results of IHC and electronic consultation in the group of benign cases. All 11 cases with the diagnosis of benignancy by electronic consultation showed a benign IHC pattern. Among 7 cases with the diagnosis of malignancy by the consultant pathologist, 5 were classified as malignant, 1 as benign, and 1 as inconclusive IHC groups. Considering problems with IHC staining of prostate needle biopsy, including loss of focus of interest, expert consultation using still images can provide very useful diagnostic information. This approach can be used as an adjunct to other diagnostic activities like IHC or even as an independent source of information to reach more accurate diagnoses in ASAP cases, particularly in institutions with limited resources.

  3. Image feature meaning for automatic key-frame extraction

    NASA Astrophysics Data System (ADS)

    Di Lecce, Vincenzo; Guerriero, Andrea

    2003-12-01

    Video abstraction and summarization, being request in several applications, has address a number of researches to automatic video analysis techniques. The processes for automatic video analysis are based on the recognition of short sequences of contiguous frames that describe the same scene, shots, and key frames representing the salient content of the shot. Since effective shot boundary detection techniques exist in the literature, in this paper we will focus our attention on key frames extraction techniques to identify the low level visual features of the frames that better represent the shot content. To evaluate the features performance, key frame automatically extracted using these features, are compared to human operator video annotations.

  4. Atypical Cell Populations Associated with Acquired Resistance to Cytostatics and Cancer Stem Cell Features: The Role of Mitochondria in Nuclear Encapsulation

    PubMed Central

    Gustmann, Sebastian; Jastrow, Holger; Acikelli, Ali Haydar; Dammann, Philip; Klein, Jacqueline; Dembinski, Ulrike; Bardenheuer, Walter; Malak, Sascha; Araúzo-Bravo, Marcos J.; Schultheis, Beate; Aldinger, Constanze; Strumberg, Dirk

    2014-01-01

    Until recently, acquired resistance to cytostatics had mostly been attributed to biochemical mechanisms such as decreased intake and/or increased efflux of therapeutics, enhanced DNA repair, and altered activity or deregulation of target proteins. Although these mechanisms have been widely investigated, little is known about membrane barriers responsible for the chemical imperviousness of cell compartments and cellular segregation in cytostatic-treated tumors. In highly heterogeneous cross-resistant and radiorefractory cell populations selected by exposure to anticancer agents, we found a number of atypical recurrent cell types in (1) tumor cell cultures of different embryonic origins, (2) mouse xenografts, and (3) paraffin sections from patient tumors. Alongside morphologic peculiarities, these populations presented cancer stem cell markers, aberrant signaling pathways, and a set of deregulated miRNAs known to confer both stem-cell phenotypes and highly aggressive tumor behavior. The first type, named spiral cells, is marked by a spiral arrangement of nuclei. The second type, monastery cells, is characterized by prominent walls inside which daughter cells can be seen maturing amid a rich mitochondrial environment. The third type, called pregnant cells, is a giant cell with a syncytium-like morphology, a main nucleus, and many endoreplicative functional progeny cells. A rare fourth cell type identified in leukemia was christened shepherd cells, as it was always associated with clusters of smaller cells. Furthermore, a portion of resistant tumor cells displayed nuclear encapsulation via mitochondrial aggregation in the nuclear perimeter in response to cytostatic insults, probably conferring imperviousness to drugs and long periods of dormancy until nuclear eclosion takes place. This phenomenon was correlated with an increase in both intracellular and intercellular mitochondrial traffic as well as with the uptake of free extracellular mitochondria. All these cellular

  5. Artificial-neural-network-based classification of mammographic microcalcifications using image structure features

    NASA Astrophysics Data System (ADS)

    Dhawan, Atam P.; Chitre, Yateen S.; Moskowitz, Myron

    1993-07-01

    Mammography associated with clinical breast examination and self-breast examination is the only effective and viable method for mass breast screening. It is however, difficult to distinguish between benign and malignant microcalcifications associated with breast cancer. Most of the techniques used in the computerized analysis of mammographic microcalcifications segment the digitized gray-level image into regions representing microcalcifications. We present a second-order gray-level histogram based feature extraction approach to extract microcalcification features. These features, called image structure features, are computed from the second-order gray-level histogram statistics, and do not require segmentation of the original image into binary regions. Several image structure features were computed for 100 cases of `difficult to diagnose' microcalcification cases with known biopsy results. These features were analyzed in a correlation study which provided a set of five best image structure features. A feedforward backpropagation neural network was used to classify mammographic microcalcifications using the image structure features. The network was trained on 10 cases of mammographic microcalcifications and tested on additional 85 `difficult-to-diagnose' microcalcifications cases using the selected image structure features. The trained network yielded good results for classification of `difficult-to- diagnose' microcalcifications into benign and malignant categories.

  6. Retroperitoneal bronchogenic cyst: a rare case showing the characteristic imaging feature of milk of calcium.

    PubMed

    Hisatomi, E; Miyajima, K; Yasumori, K; Okamura, H; Nonaka, M; Watanabe, J; Muranaka, T; Mori, H

    2003-01-01

    Bronchogenic cysts are rare congenital anomalies of the primitive foregut that are usually found above the diaphragm, and a retroperitoneal location is extremely unusual. Due to the low prevalence of these pathologies, their imaging features have seldom been described. We report a rare case of retroperitoneal bronchogenic cyst showing characteristic imaging features of milk of calcium on plain abdominal radiography and computed tomography.

  7. Functional Magnetic Resonance Imaging of Story Listening in Adolescents and Young Adults with Down Syndrome: Evidence for Atypical Neurodevelopment

    ERIC Educational Resources Information Center

    Jacola, L. M.; Byars, A. W.; Hickey, F.; Vannest, J.; Holland, S. K.; Schapiro, M. B.

    2014-01-01

    Background: Previous studies have documented differences in neural activation during language processing in individuals with Down syndrome (DS) in comparison with typically developing individuals matched for chronological age. This study used functional magnetic resonance imaging (fMRI) to compare activation during language processing in young…

  8. Wavelet Algorithm for Feature Identification and Image Analysis

    2005-10-01

    WVL are a set of python scripts based on the algorithm described in "A novel 3D wavelet-based filter for visualizing features in noisy biological data, " W. C. Moss et al., J. Microsc. 219, 43-49 (2005)

  9. Fuzzy zoning for feature matching technique in 3D reconstruction of nasal endoscopic images.

    PubMed

    Rattanalappaiboon, Surapong; Bhongmakapat, Thongchai; Ritthipravat, Panrasee

    2015-12-01

    3D reconstruction from nasal endoscopic images greatly supports an otolaryngologist in examining nasal passages, mucosa, polyps, sinuses, and nasopharyx. In general, structure from motion is a popular technique. It consists of four main steps; (1) camera calibration, (2) feature extraction, (3) feature matching, and (4) 3D reconstruction. Scale Invariant Feature Transform (SIFT) algorithm is normally used for both feature extraction and feature matching. However, SIFT algorithm relatively consumes computational time particularly in the feature matching process because each feature in an image of interest is compared with all features in the subsequent image in order to find the best matched pair. A fuzzy zoning approach is developed for confining feature matching area. Matching between two corresponding features from different images can be efficiently performed. With this approach, it can greatly reduce the matching time. The proposed technique is tested with endoscopic images created from phantoms and compared with the original SIFT technique in terms of the matching time and average errors of the reconstructed models. Finally, original SIFT and the proposed fuzzy-based technique are applied to 3D model reconstruction of real nasal cavity based on images taken from a rigid nasal endoscope. The results showed that the fuzzy-based approach was significantly faster than traditional SIFT technique and provided similar quality of the 3D models. It could be used for creating a nasal cavity taken by a rigid nasal endoscope.

  10. Key MR Imaging Features of Common Hand Surgery Conditions.

    PubMed

    de Mooij, Tristan; Riester, Scott; Kakar, Sanjeev

    2015-08-01

    The introduction of 3-T MR imaging scanners as well as dedicated wrist coils has allowed for scanning of the unique anatomic structures within the hand with unprecedented accuracy. In this article, the authors discuss common hand conditions, focusing on imaging findings and the utility of MR imaging as it pertains to hand surgery. The authors examine its role in the treatment of hand deep-space infections, scaphoid fractures, scapholunate ligament injuries, thumb ulnar collateral ligament injuries, and ulnar-sided wrist pain.

  11. Atypical vertebral Paget's disease.

    PubMed

    Beaudouin, Constance; Dohan, Anthony; Nasrallah, Toufic; Parlier, Caroline; Touraine, Sébastien; Ea, Korng; Kaci, Rachid; Laredo, Jean-Denis

    2014-07-01

    A 40-year-old Mauritanian man consulted for back pain. A computed tomography of the spine showed patchy sclerosis of the fifth and seventh thoracic vertebral bodies with normal neural arch of T5 and sclerosis and hypertrophy of the neural arch of T7, as well as diffuse sclerosis of the T11 vertebral body with a normal neural arch. At MRI, low signal-intensity on T1-weighted images and high signal-intensity on T2-weighted images involved the whole T5 and T7 vertebrae and the vertebral body of T11. Working diagnoses included metastatic disease and lymphoma, and a biopsy of T7 and then T11 was carried out. Both showed pathological findings very suggestive of Paget's disease. Since CT is usually the more specific radiological examination in vertebral Paget's disease, we thought it could be useful to report this atypical CT presentation (patchy sclerosis of the vertebral body without diffuse bone texture changes and isolated involvement of the vertebral body) of vertebral Paget's disease. PMID:24445956

  12. Non-rigid registration of medical images based on ordinal feature and manifold learning

    NASA Astrophysics Data System (ADS)

    Li, Qi; Liu, Jin; Zang, Bo

    2015-12-01

    With the rapid development of medical imaging technology, medical image research and application has become a research hotspot. This paper offers a solution to non-rigid registration of medical images based on ordinal feature (OF) and manifold learning. The structural features of medical images are extracted by combining ordinal features with local linear embedding (LLE) to improve the precision and speed of the registration algorithm. A physical model based on manifold learning and optimization search is constructed according to the complicated characteristics of non-rigid registration. The experimental results demonstrate the robustness and applicability of the proposed registration scheme.

  13. Invariant feature extraction for color image mosaic by graph card processing

    NASA Astrophysics Data System (ADS)

    Liu, Jin; Chen, Lin; Li, Deren

    2009-10-01

    Image mosaic can be widely used in remote measuring, scout in battlefield and Panasonic image demonstration. In this project, we find a general method for video (or sequence images) mosaic by techniques, such as extracting invariant features, gpu processing, multi-color feature selection, ransac algorithm for homograph matching. In order to match the image sequence automatically without influence of rotation, scale and contrast transform, local invariant feature descriptor have been extracted by graph card unit. The gpu mosaic algorithm performs very well that can be compare to slow CPU version of mosaic program with little cost time.

  14. Featured Image: Hubble's New Views of Debris Disks

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2016-09-01

    The Hubble image of a second circumstellar debris disk, HD 207917, and its best-fit model.This is a new deep observation made by Hubbles Space Telescope Imaging Spectrograph of the tilted debris disk surrounding the star HD 207129. In a recent study led by Glenn Schneider (Seward Observatory, University of Arizona), three known, nearby circumstellar disks were imaged by Hubble in order to gain a better understanding of the disks ring-like structure. The three central stars of these disks are all G-type solar analogs, and the debris rings bear many similarities to our own Kuiper belt. Imaging of debris disks like these can help us to learn more about how solar systems form around stars like our own. For more information, check out the paper below!CitationGlenn Schneider et al 2016 AJ 152 64. doi:10.3847/0004-6256/152/3/64

  15. Content-based retrieval of remote sensed images using a feature-based approach

    NASA Technical Reports Server (NTRS)

    Vellaikal, Asha; Dao, Son; Kuo, C.-C. Jay

    1995-01-01

    A feature-based representation model for content-based retrieval from a remote sensed image database is described in this work. The representation is formed by clustering spatially local pixels, and the cluster features are used to process several types of queries which are expected to occur frequently in the context of remote sensed image retrieval. Preliminary experimental results show that the feature-based representation provides a very promising tool for content-based access.

  16. Rotation and Scale Invariant Wavelet Feature for Content-Based Texture Image Retrieval.

    ERIC Educational Resources Information Center

    Lee, Moon-Chuen; Pun, Chi-Man

    2003-01-01

    Introduces a rotation and scale invariant log-polar wavelet texture feature for image retrieval. The underlying feature extraction process involves a log-polar transform followed by an adaptive row shift invariant wavelet packet transform. Experimental results show that this rotation and scale invariant wavelet feature is quite effective for image…

  17. Unsupervised Deep Feature Learning for Deformable Registration of MR Brain Images

    PubMed Central

    Wu, Guorong; Kim, Minjeong; Wang, Qian; Gao, Yaozong; Liao, Shu; Shen, Dinggang

    2014-01-01

    Establishing accurate anatomical correspondences is critical for medical image registration. Although many hand-engineered features have been proposed for correspondence detection in various registration applications, no features are general enough to work well for all image data. Although many learning-based methods have been developed to help selection of best features for guiding correspondence detection across subjects with large anatomical variations, they are often limited by requiring the known correspondences (often presumably estimated by certain registration methods) as the ground truth for training. To address this limitation, we propose using an unsupervised deep learning approach to directly learn the basis filters that can effectively represent all observed image patches. Then, the coefficients by these learnt basis filters in representing the particular image patch can be regarded as the morphological signature for correspondence detection during image registration. Specifically, a stacked two-layer convolutional network is constructed to seek for the hierarchical representations for each image patch, where the high-level features are inferred from the responses of the low-level network. By replacing the hand-engineered features with our learnt data-adaptive features for image registration, we achieve promising registration results, which demonstrates that a general approach can be built to improve image registration by using data-adaptive features through unsupervised deep learning. PMID:24579196

  18. Unsupervised deep feature learning for deformable registration of MR brain images.

    PubMed

    Wu, Guorong; Kim, Minjeong; Wang, Qian; Gao, Yaozong; Liao, Shu; Shen, Dinggang

    2013-01-01

    Establishing accurate anatomical correspondences is critical for medical image registration. Although many hand-engineered features have been proposed for correspondence detection in various registration applications, no features are general enough to work well for all image data. Although many learning-based methods have been developed to help selection of best features for guiding correspondence detection across subjects with large anatomical variations, they are often limited by requiring the known correspondences (often presumably estimated by certain registration methods) as the ground truth for training. To address this limitation, we propose using an unsupervised deep learning approach to directly learn the basis filters that can effectively represent all observed image patches. Then, the coefficients by these learnt basis filters in representing the particular image patch can be regarded as the morphological signature for correspondence detection during image registration. Specifically, a stacked two-layer convolutional network is constructed to seek for the hierarchical representations for each image patch, where the high-level features are inferred from the responses of the low-level network. By replacing the hand-engineered features with our learnt data-adaptive features for image registration, we achieve promising registration results, which demonstrates that a general approach can be built to improve image registration by using data-adaptive features through unsupervised deep learning. PMID:24579196

  19. Intensity and Range Image Based Features for Object Detection in Mobile Mapping Data

    NASA Astrophysics Data System (ADS)

    Palmer, R.; Borck, M.; West, G.; Tan, T.

    2012-07-01

    Mobile mapping is used for asset management, change detection, surveying and dimensional analysis. There is a great desire to automate these processes given the very large amounts of data, especially when 3-D point cloud data is combined with co-registered imagery - termed "3-D images". One approach requires low-level feature extraction from the images and point cloud data followed by pattern recognition and machine learning techniques to recognise the various high level features (or objects) in the images. This paper covers low-level feature analysis and investigates a number of different feature extraction methods for their usefulness. The features of interest include those based on the "bag of words" concept in which many low-level features are used e.g. histograms of gradients, as well as those describing the saliency (how unusual a region of the image is). These mainly image based features have been adapted to deal with 3-D images. The performance of the various features are discussed for typical mobile mapping scenarios and recommendations made as to the best features to use.

  20. A case of atypical progressive supranuclear palsy

    PubMed Central

    Spaccavento, Simona; Del Prete, Marina; Craca, Angela; Loverre, Anna

    2014-01-01

    Background Progressive supranuclear palsy (PSP) is a neurodegenerative extrapyramidal syndrome. Studies have demonstrated that PSP can present clinically as an atypical dementing syndrome dominated by a progressive apraxia of speech (AOS) and aphasia. Aim We aimed to investigate the clinical presentation of PSP, using a comprehensive multidimensional evaluation, and the disease response to various pharmacological treatments. Methods A 72-year-old right-handed male, with 17 years education, who first presented with aphasia, AOS, depression, apathy, and postural instability at 69 years; a complete neuropsychological evaluation, tapping the different cognitive domains, was performed. Results Testing revealed a moderate global cognitive deficit (Mini-Mental State Examination test score =20), low memory test scores (story recall, Rey’s 15-word Immediate and Delayed Recall), and poor phonemic and semantic fluency. The patient’s language was characterized by AOS, with slow speech rate, prolonged intervals between syllables and words, decreased articulatory accuracy, sound distortions, and anomia. Behavioral changes, such as depression, anxiety, apathy, and irritability, were reported. The neurological examination revealed supranuclear vertical gaze palsy, poor face miming, and a mild balance deficit. Magnetic resonance imaging showed only widespread cortical atrophy. Single photon emission computed tomography demonstrated left > right frontotemporal cortical abnormalities. After 6 months, a further neuropsychological assessment showed a progression in cognitive deficits, with additional attention deficits. The patient reported frequent falls, but the neurological deficits remained unchanged. Neuroimaging tests showed the same brain involvement. Conclusion Our case highlights the heterogeneity of the clinical features in this syndrome, demonstrating that atypical PSP can present as AOS and aphasia, without the classical features or involvement of the subcortical gray

  1. Featured Image: A Galaxy Plunges Into a Cluster Core

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2015-10-01

    The galaxy that takes up most of the frame in this stunning image (click for the full view!) is NGC 1427A. This is a dwarf irregular galaxy (unlike the fortuitously-located background spiral galaxy in the lower right corner of the image), and its currently in the process of plunging into the center of the Fornax galaxy cluster. Marcelo Mora (Pontifical Catholic University of Chile) and collaborators have analyzed observations of this galaxy made by both the Very Large Telescope in Chile and the Hubble Advanced Camera for Surveys, which produced the image shown here as a color composite in three channels. The team worked to characterize the clusters of star formation within NGC 1427A identifiable in the image as bright knots within the galaxy and determine how the interactions of this galaxy with its cluster environment affect the star formation within it. For more information and the original image, see the paper below.Citation:Marcelo D. Mora et al 2015 AJ 150 93. doi:10.1088/0004-6256/150/3/93

  2. Atypical visual saliency in autism spectrum disorder quantified through model-based eye tracking

    PubMed Central

    Wang, Shuo; Jiang, Ming; Duchesne, Xavier Morin; Laugeson, Elizabeth A.; Kennedy, Daniel P.; Adolphs, Ralph; Zhao, Qi

    2015-01-01

    Summary The social difficulties that are a hallmark of autism spectrum disorder (ASD) are thought to arise, at least in part, from atypical attention towards stimuli and their features. To investigate this hypothesis comprehensively, we characterized 700 complex natural scene images with a novel 3-layered saliency model that incorporated pixel-level (e.g., contrast), object-level (e.g., shape), and semantic-level attributes (e.g., faces) on 5551 annotated objects. Compared to matched controls, people with ASD had a stronger image center bias regardless of object distribution, reduced saliency for faces and for locations indicated by social gaze, yet a general increase in pixel-level saliency at the expense of semantic-level saliency. These results were further corroborated by direct analysis of fixation characteristics and investigation of feature interactions. Our results for the first time quantify atypical visual attention in ASD across multiple levels and categories of objects. PMID:26593094

  3. Synthetic aperture radar target detection, feature extraction, and image formation techniques

    NASA Technical Reports Server (NTRS)

    Li, Jian

    1994-01-01

    This report presents new algorithms for target detection, feature extraction, and image formation with the synthetic aperture radar (SAR) technology. For target detection, we consider target detection with SAR and coherent subtraction. We also study how the image false alarm rates are related to the target template false alarm rates when target templates are used for target detection. For feature extraction from SAR images, we present a computationally efficient eigenstructure-based 2D-MODE algorithm for two-dimensional frequency estimation. For SAR image formation, we present a robust parametric data model for estimating high resolution range signatures of radar targets and for forming high resolution SAR images.

  4. A Probabilistic Analysis of Sparse Coded Feature Pooling and Its Application for Image Retrieval

    PubMed Central

    Zhang, Yunchao; Chen, Jing; Huang, Xiujie; Wang, Yongtian

    2015-01-01

    Feature coding and pooling as a key component of image retrieval have been widely studied over the past several years. Recently sparse coding with max-pooling is regarded as the state-of-the-art for image classification. However there is no comprehensive study concerning the application of sparse coding for image retrieval. In this paper, we first analyze the effects of different sampling strategies for image retrieval, then we discuss feature pooling strategies on image retrieval performance with a probabilistic explanation in the context of sparse coding framework, and propose a modified sum pooling procedure which can improve the retrieval accuracy significantly. Further we apply sparse coding method to aggregate multiple types of features for large-scale image retrieval. Extensive experiments on commonly-used evaluation datasets demonstrate that our final compact image representation improves the retrieval accuracy significantly. PMID:26132080

  5. Quality assessment of remote sensing image fusion using feature-based fourth-order correlation coefficient

    NASA Astrophysics Data System (ADS)

    Ma, Dan; Liu, Jun; Chen, Kai; Li, Huali; Liu, Ping; Chen, Huijuan; Qian, Jing

    2016-04-01

    In remote sensing fusion, the spatial details of a panchromatic (PAN) image and the spectrum information of multispectral (MS) images will be transferred into fused images according to the characteristics of the human visual system. Thus, a remote sensing image fusion quality assessment called feature-based fourth-order correlation coefficient (FFOCC) is proposed. FFOCC is based on the feature-based coefficient concept. Spatial features related to spatial details of the PAN image and spectral features related to the spectrum information of MS images are first extracted from the fused image. Then, the fourth-order correlation coefficient between the spatial and spectral features is calculated and treated as the assessment result. FFOCC was then compared with existing widely used indices, such as Erreur Relative Globale Adimensionnelle de Synthese, and quality assessed with no reference. Results of the fusion and distortion experiments indicate that the FFOCC is consistent with subjective evaluation. FFOCC significantly outperforms the other indices in evaluating fusion images that are produced by different fusion methods and that are distorted in spatial and spectral features by blurring, adding noise, and changing intensity. All the findings indicate that the proposed method is an objective and effective quality assessment for remote sensing image fusion.

  6. Prediction of biomechanical trabecular bone properties with geometric features using MR imaging

    NASA Astrophysics Data System (ADS)

    Huber, Markus B.; Lancianese, Sarah L.; Ikpot, Imoh; Nagarajan, Mahesh B.; Lerner, Amy L.; Wismüller, Axel

    2010-03-01

    Trabecular bone parameters extracted from magnetic resonance (MR) images are compared in their ability to predict biomechanical properties determined through mechanical testing. Trabecular bone density and structural changes throughout the proximal tibia are indicative of several musculoskeletal disorders of the knee joint involving changes in the bone quality and the surrounding soft tissue. Recent studies have shown that MR imaging, most frequently applied in soft tissue imaging, also allows non-invasive 3-dimensional characterization of bone microstructure. Sophisticated MR image features that estimate local structural and geometric properties of the trabecular bone may improve the ability of MR imaging to determine local bone quality in vivo. The purpose of the current study is to use whole joint MR images to compare the performance of trabecular bone features extracted from the images in predicting biomechanical strength properties measured on the corresponding ex vivo specimens. The regional apparent bone volume fraction (appBVF) and scaling index method (SIM) derived features were calculated; a Multilayer Radial Basis Functions Network was then optimized to calculate the prediction accuracy as measured by the root mean square error (RSME) for each bone feature. The best prediction result was obtained with a SIM feature with the lowest prediction error (RSME=0.246) and the highest coefficient of determination (R2 = 0.769). The current study demonstrates that the combination of sophisticated bone structure features and supervised learning techniques can improve MR imaging as an in vivo imaging tool in determining local trabecular bone quality.

  7. Medical image retrieval system using multiple features from 3D ROIs

    NASA Astrophysics Data System (ADS)

    Lu, Hongbing; Wang, Weiwei; Liao, Qimei; Zhang, Guopeng; Zhou, Zhiming

    2012-02-01

    Compared to a retrieval using global image features, features extracted from regions of interest (ROIs) that reflect distribution patterns of abnormalities would benefit more for content-based medical image retrieval (CBMIR) systems. Currently, most CBMIR systems have been designed for 2D ROIs, which cannot reflect 3D anatomical features and region distribution of lesions comprehensively. To further improve the accuracy of image retrieval, we proposed a retrieval method with 3D features including both geometric features such as Shape Index (SI) and Curvedness (CV) and texture features derived from 3D Gray Level Co-occurrence Matrix, which were extracted from 3D ROIs, based on our previous 2D medical images retrieval system. The system was evaluated with 20 volume CT datasets for colon polyp detection. Preliminary experiments indicated that the integration of morphological features with texture features could improve retrieval performance greatly. The retrieval result using features extracted from 3D ROIs accorded better with the diagnosis from optical colonoscopy than that based on features from 2D ROIs. With the test database of images, the average accuracy rate for 3D retrieval method was 76.6%, indicating its potential value in clinical application.

  8. Bioluminescence: a versatile technique for imaging cellular and molecular features

    PubMed Central

    Paley, Miranda A.

    2016-01-01

    Bioluminescence is a ubiquitous imaging modality for visualizing biological processes in vivo. This technique employs visible light and interfaces readily with most cell and tissue types, making it a versatile technology for preclinical studies. Here we review basic bioluminescence imaging principles, along with applications of the technology that are relevant to the medicinal chemistry community. These include noninvasive cell tracking experiments, analyses of protein function, and methods to visualize small molecule metabolites. In each section, we also discuss how bioluminescent tools have revealed insights into experimental therapies and aided drug discovery. Last, we highlight the development of new bioluminescent tools that will enable more sensitive and multi-component imaging experiments and, thus, expand our broader understanding of living systems. PMID:27594981

  9. Spectral feature variations in x-ray diffraction imaging systems

    NASA Astrophysics Data System (ADS)

    Wolter, Scott D.; Greenberg, Joel A.

    2016-05-01

    Materials with different atomic or molecular structures give rise to unique scatter spectra when measured by X-ray diffraction. The details of these spectra, though, can vary based on both intrinsic (e.g., degree of crystallinity or doping) and extrinsic (e.g., pressure or temperature) conditions. While this sensitivity is useful for detailed characterizations of the material properties, these dependences make it difficult to perform more general classification tasks, such as explosives threat detection in aviation security. A number of challenges, therefore, currently exist for reliable substance detection including the similarity in spectral features among some categories of materials combined with spectral feature variations from materials processing and environmental factors. These factors complicate the creation of a material dictionary and the implementation of conventional classification and detection algorithms. Herein, we report on two prominent factors that lead to variations in spectral features: crystalline texture and temperature variations. Spectral feature comparisons between materials categories will be described for solid metallic sheet, aqueous liquids, polymer sheet, and metallic, organic, and inorganic powder specimens. While liquids are largely immune to texture effects, they are susceptible to temperature changes that can modify their density or produce phase changes. We will describe in situ temperature-dependent measurement of aqueous-based commercial goods in the temperature range of -20°C to 35°C.

  10. A new method to extract stable feature points based on self-generated simulation images

    NASA Astrophysics Data System (ADS)

    Long, Fei; Zhou, Bin; Ming, Delie; Tian, Jinwen

    2015-10-01

    Recently, image processing has got a lot of attention in the field of photogrammetry, medical image processing, etc. Matching two or more images of the same scene taken at different times, by different cameras, or from different viewpoints, is a popular and important problem. Feature extraction plays an important part in image matching. Traditional SIFT detectors reject the unstable points by eliminating the low contrast and edge response points. The disadvantage is the need to set the threshold manually. The main idea of this paper is to get the stable extremums by machine learning algorithm. Firstly we use ASIFT approach coupled with the light changes and blur to generate multi-view simulated images, which make up the set of the simulated images of the original image. According to the way of generating simulated images set, affine transformation of each generated image is also known. Instead of the traditional matching process which contain the unstable RANSAC method to get the affine transformation, this approach is more stable and accurate. Secondly we calculate the stability value of the feature points by the set of image with its affine transformation. Then we get the different feature properties of the feature point, such as DOG features, scales, edge point density, etc. Those two form the training set while stability value is the dependent variable and feature property is the independent variable. At last, a process of training by Rank-SVM is taken. We will get a weight vector. In use, based on the feature properties of each points and weight vector calculated by training, we get the sort value of each feature point which refers to the stability value, then we sort the feature points. In conclusion, we applied our algorithm and the original SIFT detectors to test as a comparison. While in different view changes, blurs, illuminations, it comes as no surprise that experimental results show that our algorithm is more efficient.

  11. The relationship study between image features and detection probability based on psychology experiments

    NASA Astrophysics Data System (ADS)

    Lin, Wei; Chen, Yu-hua; Wang, Ji-yuan; Gao, Hong-sheng; Wang, Ji-jun; Su, Rong-hua; Mao, Wei

    2011-04-01

    Detection probability is an important index to represent and estimate target viability, which provides basis for target recognition and decision-making. But it will expend a mass of time and manpower to obtain detection probability in reality. At the same time, due to the different interpretation of personnel practice knowledge and experience, a great difference will often exist in the datum obtained. By means of studying the relationship between image features and perception quantity based on psychology experiments, the probability model has been established, in which the process is as following.Firstly, four image features have been extracted and quantified, which affect directly detection. Four feature similarity degrees between target and background were defined. Secondly, the relationship between single image feature similarity degree and perception quantity was set up based on psychological principle, and psychological experiments of target interpretation were designed which includes about five hundred people for interpretation and two hundred images. In order to reduce image features correlativity, a lot of artificial synthesis images have been made which include images with single brightness feature difference, images with single chromaticity feature difference, images with single texture feature difference and images with single shape feature difference. By analyzing and fitting a mass of experiments datum, the model quantitys have been determined. Finally, by applying statistical decision theory and experimental results, the relationship between perception quantity with target detection probability has been found. With the verification of a great deal of target interpretation in practice, the target detection probability can be obtained by the model quickly and objectively.

  12. Malformations of cortical development: 3T magnetic resonance imaging features

    PubMed Central

    Battal, Bilal; Ince, Selami; Akgun, Veysel; Kocaoglu, Murat; Ozcan, Emrah; Tasar, Mustafa

    2015-01-01

    Malformation of cortical development (MCD) is a term representing an inhomogeneous group of central nervous system abnormalities, referring particularly to embriyological aspect as a consequence of any of the three developmental stages, i.e., cell proliferation, cell migration and cortical organization. These include cotical dysgenesis, microcephaly, polymicrogyria, schizencephaly, lissencephaly, hemimegalencephaly, heterotopia and focal cortical dysplasia. Since magnetic resonance imaging is the modality of choice that best identifies the structural anomalies of the brain cortex, we aimed to provide a mini review of MCD by using 3T magnetic resonance scanner images. PMID:26516429

  13. Budd-Chiari syndrome: an update on imaging features.

    PubMed

    Faraoun, Sid Ahmed; Boudjella, Mohamed El Amine; Debzi, Nabil; Benidir, Naima; Afredj, Nawel; Guerrache, Youcef; Bentabak, Kamel; Soyer, Philippe; Bendib, Salah Eddine

    2016-01-01

    Budd-Chiari syndrome (BCS) is a rare cause of portal hypertension and liver failure. This condition is characterized by an impaired hepatic venous drainage. The diagnosis of BCS is based on imaging, which helps initiate treatment. Imaging findings can be categorized into direct and indirect signs. Direct signs are the hallmarks of BCS and consist of visualization of obstructive lesions of the hepatic veins or the upper portion of the inferior vena cava. Indirect signs, which are secondary to venous obstruction, correspond to intra- and extrahepatic collateral circulation, perfusion abnormalities, dysmorphy and signs of portal hypertension. PMID:27317208

  14. Medical image retrieval based on texture and shape feature co-occurrence

    NASA Astrophysics Data System (ADS)

    Zhou, Yixiao; Huang, Yan; Ling, Haibin; Peng, Jingliang

    2012-03-01

    With the rapid development and wide application of medical imaging technology, explosive volumes of medical image data are produced every day all over the world. As such, it becomes increasingly challenging to manage and utilize such data effectively and efficiently. In particular, content-based medical image retrieval has been intensively researched in the past decade or so. In this work, we propose a novel approach to content-based medical image retrieval utilizing the co-occurrence of both the texture and the shape features in contrast to most previous algorithms that use purely the texture or the shape feature. Specifically, we propose a novel form of representation for the co-occurrence of the texture and the shape features in an image, i.e., the gray level and edge direction co-occurrence matrix (GLEDCOM). Based on GLEDCOM, we define eleven features forming a feature vector that is used to measure the similarity between images. As a result, it consistently yields outstanding performance on both images rich in texture (e.g., image of brain) and images with dominant smooth regions and sharp edges (e.g., image of bladder). As demonstrated by experiments, the mean precision of retrieval with GLEDCOM algorithm outperforms a set of representative algorithms including the gray level co-occurrence matrix (GLCM) based, the Hu's seven moment invariants (HSMI) based, the uniformity estimation method (UEM) based and the the modified Zernike moments (MZM) based algorithms by 10%-20%.

  15. Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image.

    PubMed

    Singh, Anushikha; Dutta, Malay Kishore; ParthaSarathi, M; Uher, Vaclav; Burget, Radim

    2016-02-01

    Glaucoma is a disease of the retina which is one of the most common causes of permanent blindness worldwide. This paper presents an automatic image processing based method for glaucoma diagnosis from the digital fundus image. In this paper wavelet feature extraction has been followed by optimized genetic feature selection combined with several learning algorithms and various parameter settings. Unlike the existing research works where the features are considered from the complete fundus or a sub image of the fundus, this work is based on feature extraction from the segmented and blood vessel removed optic disc to improve the accuracy of identification. The experimental results presented in this paper indicate that the wavelet features of the segmented optic disc image are clinically more significant in comparison to features of the whole or sub fundus image in the detection of glaucoma from fundus image. Accuracy of glaucoma identification achieved in this work is 94.7% and a comparison with existing methods of glaucoma detection from fundus image indicates that the proposed approach has improved accuracy of classification.

  16. Featured Image: A Supernova Remnant in X-Rays

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2015-09-01

    This is a three-color X-ray image taken by Chandra of the supernova remnant RCW 103. This supernova remnant is an unusual system: its young, but unlike other remnants of its age, metal-rich ejecta hadnt previously been discovered in it. In this paper, Kari Frank (Pennsylvania State University) and collaborators analyze the three deepest Chandra observations of RCW 103 and find the first evidence for metal-rich ejecta emission scattered throughout the remnant. Their analyses also help to constrain the identity of the mysterious compact stellar object powering the remnant. In this image, red = 0.30.85 keV, green = 0.851.70 keV, and blue = 1.73.0 keV; click on the image for the full view. For more information and the original image, see the paper here:Kari A. Frank et al 2015 ApJ 810 113 doi:10.1088/0004-637X/810/2/113.

  17. Retroperitoneal oncocytoma: case report and review of the imaging features.

    PubMed

    Roy, A A; Jameson, C; Christmas, T J; Aslam Sohaib, S

    2011-08-01

    Oncocytomas are uncommon tumours that occur in a number of specific anatomical locations within the head, neck, chest, abdomen and pelvis. When occurring in the retroperitoneum, oncocytomas almost always arise from either the kidney or adrenal gland. With this case we present the imaging findings of an exceptionally rare retroperitoneal oncocytoma whose site of origin is neither the kidney nor adrenal gland.

  18. A new and fast image feature selection method for developing an optimal mammographic mass detection scheme

    PubMed Central

    Tan, Maxine; Pu, Jiantao; Zheng, Bin

    2014-01-01

    Purpose: Selecting optimal features from a large image feature pool remains a major challenge in developing computer-aided detection (CAD) schemes of medical images. The objective of this study is to investigate a new approach to significantly improve efficacy of image feature selection and classifier optimization in developing a CAD scheme of mammographic masses. Methods: An image dataset including 1600 regions of interest (ROIs) in which 800 are positive (depicting malignant masses) and 800 are negative (depicting CAD-generated false positive regions) was used in this study. After segmentation of each suspicious lesion by a multilayer topographic region growth algorithm, 271 features were computed in different feature categories including shape, texture, contrast, isodensity, spiculation, local topological features, as well as the features related to the presence and location of fat and calcifications. Besides computing features from the original images, the authors also computed new texture features from the dilated lesion segments. In order to select optimal features from this initial feature pool and build a highly performing classifier, the authors examined and compared four feature selection methods to optimize an artificial neural network (ANN) based classifier, namely: (1) Phased Searching with NEAT in a Time-Scaled Framework, (2) A sequential floating forward selection (SFFS) method, (3) A genetic algorithm (GA), and (4) A sequential forward selection (SFS) method. Performances of the four approaches were assessed using a tenfold cross validation method. Results: Among these four methods, SFFS has highest efficacy, which takes 3%–5% of computational time as compared to GA approach, and yields the highest performance level with the area under a receiver operating characteristic curve (AUC) = 0.864 ± 0.034. The results also demonstrated that except using GA, including the new texture features computed from the dilated mass segments improved the AUC

  19. Extraction of informative cell features by segmentation of densely clustered tissue images.

    PubMed

    Kothari, Sonal; Chaudry, Qaiser; Wang, May D

    2009-01-01

    This paper presents a fast methodology for the estimation of informative cell features from densely clustered RGB tissue images. The features estimated include nuclei count, nuclei size distribution, nuclei eccentricity (roundness) distribution, nuclei closeness distribution and cluster size distribution. Our methodology is a three step technique. Firstly, we generate a binary nuclei mask from an RGB tissue image by color segmentation. Secondly, we segment nuclei clusters present in the binary mask into individual nuclei by concavity detection and ellipse fitting. Finally, we estimate informative features for every nuclei and their distribution for the complete image. The main focus of our work is the development of a fast and accurate nuclei cluster segmentation technique for densely clustered tissue images. We also developed a simple graphical user interface (GUI) for our application which requires minimal user interaction and can efficiently extract features from nuclei clusters, making it feasible for clinical applications (less than 2 minutes for a 1.9 megapixel tissue image).

  20. Automatic parameter selection for feature-based multi-sensor image registration

    NASA Astrophysics Data System (ADS)

    DelMarco, Stephen; Tom, Victor; Webb, Helen; Chao, Alan

    2006-05-01

    Accurate image registration is critical for applications such as precision targeting, geo-location, change-detection, surveillance, and remote sensing. However, the increasing volume of image data is exceeding the current capacity of human analysts to perform manual registration. This image data glut necessitates the development of automated approaches to image registration, including algorithm parameter value selection. Proper parameter value selection is crucial to the success of registration techniques. The appropriate algorithm parameters can be highly scene and sensor dependent. Therefore, robust algorithm parameter value selection approaches are a critical component of an end-to-end image registration algorithm. In previous work, we developed a general framework for multisensor image registration which includes feature-based registration approaches. In this work we examine the problem of automated parameter selection. We apply the automated parameter selection approach of Yitzhaky and Peli to select parameters for feature-based registration of multisensor image data. The approach consists of generating multiple feature-detected images by sweeping over parameter combinations and using these images to generate estimated ground truth. The feature-detected images are compared to the estimated ground truth images to generate ROC points associated with each parameter combination. We develop a strategy for selecting the optimal parameter set by choosing the parameter combination corresponding to the optimal ROC point. We present numerical results showing the effectiveness of the approach using registration of collected SAR data to reference EO data.

  1. Classification of yeast cells from image features to evaluate pathogen conditions

    NASA Astrophysics Data System (ADS)

    van der Putten, Peter; Bertens, Laura; Liu, Jinshuo; Hagen, Ferry; Boekhout, Teun; Verbeek, Fons J.

    2007-01-01

    Morphometrics from images, image analysis, may reveal differences between classes of objects present in the images. We have performed an image-features-based classification for the pathogenic yeast Cryptococcus neoformans. Building and analyzing image collections from the yeast under different environmental or genetic conditions may help to diagnose a new "unseen" situation. Diagnosis here means that retrieval of the relevant information from the image collection is at hand each time a new "sample" is presented. The basidiomycetous yeast Cryptococcus neoformans can cause infections such as meningitis or pneumonia. The presence of an extra-cellular capsule is known to be related to virulence. This paper reports on the approach towards developing classifiers for detecting potentially more or less virulent cells in a sample, i.e. an image, by using a range of features derived from the shape or density distribution. The classifier can henceforth be used for automating screening and annotating existing image collections. In addition we will present our methods for creating samples, collecting images, image preprocessing, identifying "yeast cells" and creating feature extraction from the images. We compare various expertise based and fully automated methods of feature selection and benchmark a range of classification algorithms and illustrate successful application to this particular domain.

  2. Feature-based face representations and image reconstruction from behavioral and neural data

    PubMed Central

    Nestor, Adrian; Plaut, David C.; Behrmann, Marlene

    2016-01-01

    The reconstruction of images from neural data can provide a unique window into the content of human perceptual representations. Although recent efforts have established the viability of this enterprise using functional magnetic resonance imaging (MRI) patterns, these efforts have relied on a variety of prespecified image features. Here, we take on the twofold task of deriving features directly from empirical data and of using these features for facial image reconstruction. First, we use a method akin to reverse correlation to derive visual features from functional MRI patterns elicited by a large set of homogeneous face exemplars. Then, we combine these features to reconstruct novel face images from the corresponding neural patterns. This approach allows us to estimate collections of features associated with different cortical areas as well as to successfully match image reconstructions to corresponding face exemplars. Furthermore, we establish the robustness and the utility of this approach by reconstructing images from patterns of behavioral data. From a theoretical perspective, the current results provide key insights into the nature of high-level visual representations, and from a practical perspective, these findings make possible a broad range of image-reconstruction applications via a straightforward methodological approach. PMID:26711997

  3. Two-stage high resolution remote sensing image retrieval combining semantic and visual features

    NASA Astrophysics Data System (ADS)

    Wan, Qi-Ming; Wang, Min; Zhang, Xing-Yue; Zhang, Da-Qian

    2009-10-01

    In this work, we put forward a two-stage image retrieval methodology by integrating high level image semantic features and low level visual features. At the first stage, we segment an image into parcels using a multiresolution remotely sensed image segmentation method combining rainfalling watershed algorithm and fast region merging. We then classify these parcels with Support Vector Machine (SVM), a famous non-linear classification scheme to connect the low-level visual features with high-level semantic features. These classes are then stored in semantic features databases for future use. When users carry out their rough semantic retrieval, they should choose and combine these semantic classes, and our method returns some image blocks which include the interested classes as the first "rough" retrieval results. At the second stage users should select an example from the results. We then construct and compare the similarity between the color and texture histograms for both the query example and each one in the semantic retrieval result. If the total similarity is higher than some threshold, the image will be returned as a suitable retrieval result. These images are sorted according their similarity as the final retrieval results. Experiments indicate our approach can get more effective and accurate results than content-based image retrieval only using visual features.

  4. Image Geo-Localization Based on Multiple Nearest Neighbor Feature Matching Using Generalized Graphs.

    PubMed

    Zamir, Amir Roshan; Shah, Mubarak

    2014-08-01

    In this paper, we present a new framework for geo-locating an image utilizing a novel multiple nearest neighbor feature matching method using Generalized Minimum Clique Graphs (GMCP). First, we extract local features (e.g., SIFT) from the query image and retrieve a number of nearest neighbors for each query feature from the reference data set. Next, we apply our GMCP-based feature matching to select a single nearest neighbor for each query feature such that all matches are globally consistent. Our approach to feature matching is based on the proposition that the first nearest neighbors are not necessarily the best choices for finding correspondences in image matching. Therefore, the proposed method considers multiple reference nearest neighbors as potential matches and selects the correct ones by enforcing consistency among their global features (e.g., GIST) using GMCP. In this context, we argue that using a robust distance function for finding the similarity between the global features is essential for the cases where the query matches multiple reference images with dissimilar global features. Towards this end, we propose a robust distance function based on the Gaussian Radial Basis Function (G-RBF). We evaluated the proposed framework on a new data set of 102k street view images; the experiments show it outperforms the state of the art by 10 percent.

  5. Document image retrieval with morphology-based segmentation and features combination

    NASA Astrophysics Data System (ADS)

    Bockholt, Tiago C.; Cavalcanti, George D. C.; Mello, Carlos A. B.

    2011-01-01

    Digital libraries need more than just a retrieval based on keywords, which can be inefficient for some applications. Thus, a document retrieval based on content of the digitized image version of the document can be a more appropriated approach. This paper discusses the retrieval of document images by means of identifying a variety of elements present in the document's image body. We propose a new strategy to identify and combine features extracted from a document image. We also consider the task of constructing an optimized feature set to improve the retrieval performance and to validate our experiments on an assorted database. Experimental results show that the proposed segmentation together with a wisely feature combination increase the overall retrieval performance. Moreover the retrieved images demonstrate the generality and effectiveness of our approach for an efficient segmentation and classification of document images.

  6. Improving image retrieval using combined features of Hough transform and Zernike moments

    NASA Astrophysics Data System (ADS)

    Singh, Chandan; Pooja

    2011-12-01

    In this paper, a novel solution to content based image retrieval system is provided by considering both local and global features of the images. Local features extraction is done by computing histograms of distances from edge lines to the centroid of edge image, where edge lines are detected using Hough transform. It is a robust and effective method to provide association among adjacent edge points, which represent their linear relationship with each other. Zernike moments are used to describe the global features. We have applied algorithms for the fast computation of Hough transform and Zernike moments to make our system fast and efficient. Bray-Curtis similarity measure is applied to compute the similarity among images. A large number of experiments are carried out to evaluate the system performance over six standard databases, which represent various kinds of images. The results reveal that the proposed descriptors and the Bray-Curtis distance measure outperform the existing methods of image retrieval.

  7. Featured Image: Violent History of the Toothbrush Cluster

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2016-03-01

    This stunning composite image shows the components of the galaxy cluster RX J0603.3+4214, located at a redshift of z=0.225. This image contains Chandra X-ray data (red), radio data from the Giant Metrewave Radio Telescope (green), and optical from the Subaru Telescope (background). The shape of the enormous (6.5 million light-years across!) radio relic, shown in green, gives this collection of galaxies its nickname: the Toothbrush Cluster. A team of scientists led by Myungkook James Jee (Yonsei University and University of California, Davis) used Hubble and Subaru to study weak gravitational lensing by the Toothbrush Cluster, in order to determine how the clusters mass is distributed. Jee and collaborators found that most of the dark-matter mass is located in two large clumps on a north-south axis (shown by the white contours overlaid on the image), suggesting that the Toothbrush Cluster is the result of a past merger between two clusters. This violent merger is likely what caused the enormous Toothbrush radio relic. Check out the paper below for more information!CitationM. James Jee et al 2016 ApJ 817 179. doi:10.3847/0004-637X/817/2/179

  8. Luminescent Silica Nanoparticles Featuring Collective Processes for Optical Imaging.

    PubMed

    Rampazzo, Enrico; Prodi, Luca; Petrizza, Luca; Zaccheroni, Nelsi

    2016-01-01

    The field of nanoparticles has successfully merged with imaging to optimize contrast agents for many detection techniques. This combination has yielded highly positive results, especially in optical and magnetic imaging, leading to diagnostic methods that are now close to clinical use. Biological sciences have been taking advantage of luminescent labels for many years and the development of luminescent nanoprobes has helped definitively in making the crucial step forward in in vivo applications. To this end, suitable probes should present excitation and emission within the NIR region where tissues have minimal absorbance. Among several nanomaterials engineered with this aim, including noble metal, lanthanide, and carbon nanoparticles and quantum dots, we have focused our attention here on luminescent silica nanoparticles. Many interesting results have already been obtained with nanoparticles containing only one kind of photophysically active moiety. However, the presence of different emitting species in a single nanoparticle can lead to diverse properties including cooperative behaviours. We present here the state of the art in the field of silica luminescent nanoparticles exploiting collective processes to obtain ultra-bright units suitable as contrast agents in optical imaging and optical sensing and for other high sensitivity applications.

  9. [Spectral curve shape feature-based hyperspectral remote sensing image retrieval].

    PubMed

    Li, Fei; Zhou, Cheng-Hu; Chen, Rong-Guo

    2008-11-01

    With the rapid development of technology of sensors and data transmission, using all kinds of airplane sensors and satellite sensors, the authors can get different voluminous remote sensing image data of earth. Those voluminous remote sensing image data bring problems of data storage and management. It is becoming increasingly necessary to retrieve some information the authors need from those voluminous image data. Image retrieval was proposed by CHANG firstly in 1980 and can be regarded as expansion of traditional information retrieval. Oriented to the demands of efficient retrieval for voluminous remote sensing image, and considering that there are many bands in hyperspectral remote sensing image, the authors first analyzed image distance function and similarity measure in image retrieval. The most crucial issues in retrieval are spectral features extraction and similarity measure. In the present paper, the authors used classical Douglas-Peucker algorithm (hereinafter referred to DP algorithm) for curve simplification to extract shape features of spectral curve, in order to speed up hyperspectral remote sensing image retrieval. And the authors proposed a new method of spectral curve and remote sensing image retrieval, called Douglas-Peucker Spectral Retrieval algorithm (hereinafter referred to DPSR algorithm). Spectral shape features were used in image retrieval. DPSR used features of spectral curve, reduced the computation amount, realized match and retrieval efficiently, and is suitable for spectral curve retrieval in hyperspectral remote sensing image. The authors selected four ground features (grass, apple garden, grape garden and pond) in OMISI hyperspectral remote sensing image to compute similarity measure results, in order to test the effect of DPSR algorithm. Compared with traditional analysis method such as spectral angle match (SAM) and spectral information divergence (SID), DPSR can maintain high precision of results with less amount of computation

  10. [Classification technique for hyperspectral image based on subspace of bands feature extraction and LS-SVM].

    PubMed

    Gao, Heng-zhen; Wan, Jian-wei; Zhu, Zhen-zhen; Wang, Li-bao; Nian, Yong-jian

    2011-05-01

    The present paper proposes a novel hyperspectral image classification algorithm based on LS-SVM (least squares support vector machine). The LS-SVM uses the features extracted from subspace of bands (SOB). The maximum noise fraction (MNF) method is adopted as the feature extraction method. The spectral correlations of the hyperspectral image are used in order to divide the feature space into several SOBs. Then the MNF is used to extract characteristic features of the SOBs. The extracted features are combined into the feature vector for classification. So the strong bands correlation is avoided and the spectral redundancies are reduced. The LS-SVM classifier is adopted, which replaces inequality constraints in SVM by equality constraints. So the computation consumption is reduced and the learning performance is improved. The proposed method optimizes spectral information by feature extraction and reduces the spectral noise. The classifier performance is improved. Experimental results show the superiorities of the proposed algorithm.

  11. Clinical and color Doppler imaging features of one patient with occult giant cell arteritis presenting arteritic anterior ischemic optic neuropathy.

    PubMed

    Jianu, Dragoş Cătălin; Jianu, Silviana Nina; Petrica, Ligia; Motoc, Andrei Gheorghe Marius; Dan, Traian Flavius; Lăzureanu, Dorela CodruŢa; Munteanu, Mihnea

    2016-01-01

    Anterior ischemic optic neuropathies (AIONs) represent a segmental infarction of the optic nerve head (ONH) supplied by the posterior ciliary arteries (PCAs). Blood supply blockage can occur with or without arterial inflammation. For this reason, there are two types of AIONs: non-arteritic (NA-AION), and arteritic (A-AION), the latter is almost invariably due to giant cell arteritis (GCA). GCA is a primary vasculitis that predominantly affects extracranial medium-sized arteries, particularly the branches of the external carotid arteries (including superficial temporal arteries - TAs). One patient with clinical suspicion of acute left AION was examined at admission following a complex protocol including color Doppler imaging (CDI) of orbital vessels, and color duplex sonography of the TAs and of the carotid arteries. She presented an equivocal combination of an abrupt, painless, and severe vision loss in the left eye, and an atypical diffuse hyperemic left optic disc edema. She had characteristic CDI features for GCA with eye involvement: high resistance index, with absent, or severe diminished blood flow velocities, especially end-diastolic velocities, in all orbital vessels, especially on the left side (A-AION). Typical sonographic feature in temporal arteritis as part of GCA was "dark halo" sign. On the other hand, she did not present classic clinical or systemic symptoms of GCA: temporal headache, tender TAs, malaise (occult GCA). The left TA biopsy confirmed the diagnosis of GCA. The ultrasound investigations enabled prompt differentiation between NA-AION and A-AION, the later requiring in her case immediate steroid treatment, to prevent further visual loss in the right eye. PMID:27516038

  12. Automatic classification of hepatocellular carcinoma images based on nuclear and structural features

    NASA Astrophysics Data System (ADS)

    Kiyuna, Tomoharu; Saito, Akira; Marugame, Atsushi; Yamashita, Yoshiko; Ogura, Maki; Cosatto, Eric; Abe, Tokiya; Hashiguchi, Akinori; Sakamoto, Michiie

    2013-03-01

    Diagnosis of hepatocellular carcinoma (HCC) on the basis of digital images is a challenging problem because, unlike gastrointestinal carcinoma, strong structural and morphological features are limited and sometimes absent from HCC images. In this study, we describe the classification of HCC images using statistical distributions of features obtained from image analysis of cell nuclei and hepatic trabeculae. Images of 130 hematoxylin-eosin (HE) stained histologic slides were captured at 20X by a slide scanner (Nanozoomer, Hamamatsu Photonics, Japan) and 1112 regions of interest (ROI) images were extracted for classification (551 negatives and 561 positives, including 113 well-differentiated positives). For a single nucleus, the following features were computed: area, perimeter, circularity, ellipticity, long and short axes of elliptic fit, contour complexity and gray level cooccurrence matrix (GLCM) texture features (angular second moment, contrast, homogeneity and entropy). In addition, distributions of nuclear density and hepatic trabecula thickness within an ROI were also extracted. To represent an ROI, statistical distributions (mean, standard deviation and percentiles) of these features were used. In total, 78 features were extracted for each ROI and a support vector machine (SVM) was trained to classify negative and positive ROIs. Experimental results using 5-fold cross validation show 90% sensitivity for an 87.8% specificity. The use of statistical distributions over a relatively large area makes the HCC classifier robust to occasional failures in the extraction of nuclear or hepatic trabecula features, thus providing stability to the system.

  13. Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images.

    PubMed

    Chu, Carlton; Hsu, Ai-Ling; Chou, Kun-Hsien; Bandettini, Peter; Lin, Chingpo

    2012-03-01

    There are growing numbers of studies using machine learning approaches to characterize patterns of anatomical difference discernible from neuroimaging data. The high-dimensionality of image data often raises a concern that feature selection is needed to obtain optimal accuracy. Among previous studies, mostly using fixed sample sizes, some show greater predictive accuracies with feature selection, whereas others do not. In this study, we compared four common feature selection methods. 1) Pre-selected region of interests (ROIs) that are based on prior knowledge. 2) Univariate t-test filtering. 3) Recursive feature elimination (RFE), and 4) t-test filtering constrained by ROIs. The predictive accuracies achieved from different sample sizes, with and without feature selection, were compared statistically. To demonstrate the effect, we used grey matter segmented from the T1-weighted anatomical scans collected by the Alzheimer's disease Neuroimaging Initiative (ADNI) as the input features to a linear support vector machine classifier. The objective was to characterize the patterns of difference between Alzheimer's disease (AD) patients and cognitively normal subjects, and also to characterize the difference between mild cognitive impairment (MCI) patients and normal subjects. In addition, we also compared the classification accuracies between MCI patients who converted to AD and MCI patients who did not convert within the period of 12 months. Predictive accuracies from two data-driven feature selection methods (t-test filtering and RFE) were no better than those achieved using whole brain data. We showed that we could achieve the most accurate characterizations by using prior knowledge of where to expect neurodegeneration (hippocampus and parahippocampal gyrus). Therefore, feature selection does improve the classification accuracies, but it depends on the method adopted. In general, larger sample sizes yielded higher accuracies with less advantage obtained by using

  14. Pediatric Cerebellar Tumors: Emerging Imaging Techniques and Advances in Understanding of Genetic Features.

    PubMed

    Choudhri, Asim F; Siddiqui, Adeel; Klimo, Paul

    2016-08-01

    Cerebellar tumors are the most common group of solid tumors in children. MR imaging provides an important role in characterization of these lesions, surgical planning, and postsurgical surveillance. Preoperative imaging can help predict the histologic subtype of tumors, which can provide guidance for surgical planning. Beyond histology, pediatric brain tumors are undergoing new classification schemes based on genetic features. Intraoperative MR imaging has emerged as an important tool in the surgical management of pediatric brain tumors. Effective understanding of the imaging features of pediatric cerebellar tumors can benefit communication with neurosurgeons and neuro-oncologists and can improve patient management.

  15. Pediatric Cerebellar Tumors: Emerging Imaging Techniques and Advances in Understanding of Genetic Features.

    PubMed

    Choudhri, Asim F; Siddiqui, Adeel; Klimo, Paul

    2016-08-01

    Cerebellar tumors are the most common group of solid tumors in children. MR imaging provides an important role in characterization of these lesions, surgical planning, and postsurgical surveillance. Preoperative imaging can help predict the histologic subtype of tumors, which can provide guidance for surgical planning. Beyond histology, pediatric brain tumors are undergoing new classification schemes based on genetic features. Intraoperative MR imaging has emerged as an important tool in the surgical management of pediatric brain tumors. Effective understanding of the imaging features of pediatric cerebellar tumors can benefit communication with neurosurgeons and neuro-oncologists and can improve patient management. PMID:27423803

  16. ROC-Boosting: A Feature Selection Method for Health Identification Using Tongue Image

    PubMed Central

    Cui, Yan; Liao, Shizhong; Wang, Hongwu

    2015-01-01

    Objective. To select significant Haar-like features extracted from tongue images for health identification. Materials and Methods. 1,322 tongue cases were included in this study. Health information and tongue images of each case were collected. Cases were classified into the following groups: group containing 148 cases diagnosed as health; group containing 332 cases diagnosed as ill based on health information, even though tongue image is normal; and group containing 842 cases diagnosed as ill. Haar-like features were extracted from tongue images. Then, we proposed a new boosting method in the ROC space for selecting significant features from the features extracted from these images. Results. A total of 27 features were obtained from groups A, B, and C. Seven features were selected from groups A and B, while 25 features were selected from groups A and C. Conclusions. The selected features in this study were mainly obtained from the root, top, and side areas of the tongue. This is consistent with the tongue partitions employed in traditional Chinese medicine. These results provide scientific evidence to TCM tongue diagnosis for health identification. PMID:26543494

  17. NOTE: Prostate cancer multi-feature analysis using trans-rectal ultrasound images

    NASA Astrophysics Data System (ADS)

    Mohamed, S. S.; Salama, M. M. A.; Kamel, M.; El-Saadany, E. F.; Rizkalla, K.; Chin, J.

    2005-08-01

    This note focuses on extracting and analysing prostate texture features from trans-rectal ultrasound (TRUS) images for tissue characterization. One of the principal contributions of this investigation is the use of the information of the images' frequency domain features and spatial domain features to attain a more accurate diagnosis. Each image is divided into regions of interest (ROIs) by the Gabor multi-resolution analysis, a crucial stage, in which segmentation is achieved according to the frequency response of the image pixels. The pixels with a similar response to the same filter are grouped to form one ROI. Next, from each ROI two different statistical feature sets are constructed; the first set includes four grey level dependence matrix (GLDM) features and the second set consists of five grey level difference vector (GLDV) features. These constructed feature sets are then ranked by the mutual information feature selection (MIFS) algorithm. Here, the features that provide the maximum mutual information of each feature and class (cancerous and non-cancerous) and the minimum mutual information of the selected features are chosen, yeilding a reduced feature subset. The two constructed feature sets, GLDM and GLDV, as well as the reduced feature subset, are examined in terms of three different classifiers: the condensed k-nearest neighbour (CNN), the decision tree (DT) and the support vector machine (SVM). The accuracy classification results range from 87.5% to 93.75%, where the performance of the SVM and that of the DT are significantly better than the performance of the CNN.

  18. Automatic brain MR image denoising based on texture feature-based artificial neural networks.

    PubMed

    Chang, Yu-Ning; Chang, Herng-Hua

    2015-01-01

    Noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing in brain magnetic resonance (MR) image analysis such as tissue classification, segmentation and registration. Accordingly, noise removal in brain MR images is important for a wide variety of subsequent processing applications. However, most existing denoising algorithms require laborious tuning of parameters that are often sensitive to specific image features and textures. Automation of these parameters through artificial intelligence techniques will be highly beneficial. In the present study, an artificial neural network associated with image texture feature analysis is proposed to establish a predictable parameter model and automate the denoising procedure. In the proposed approach, a total of 83 image attributes were extracted based on four categories: 1) Basic image statistics. 2) Gray-level co-occurrence matrix (GLCM). 3) Gray-level run-length matrix (GLRLM) and 4) Tamura texture features. To obtain the ranking of discrimination in these texture features, a paired-samples t-test was applied to each individual image feature computed in every image. Subsequently, the sequential forward selection (SFS) method was used to select the best texture features according to the ranking of discrimination. The selected optimal features were further incorporated into a back propagation neural network to establish a predictable parameter model. A wide variety of MR images with various scenarios were adopted to evaluate the performance of the proposed framework. Experimental results indicated that this new automation system accurately predicted the bilateral filtering parameters and effectively removed the noise in a number of MR images. Comparing to the manually tuned filtering process, our approach not only produced better denoised results but also saved significant processing time.

  19. Automatic brain MR image denoising based on texture feature-based artificial neural networks.

    PubMed

    Chang, Yu-Ning; Chang, Herng-Hua

    2015-01-01

    Noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing in brain magnetic resonance (MR) image analysis such as tissue classification, segmentation and registration. Accordingly, noise removal in brain MR images is important for a wide variety of subsequent processing applications. However, most existing denoising algorithms require laborious tuning of parameters that are often sensitive to specific image features and textures. Automation of these parameters through artificial intelligence techniques will be highly beneficial. In the present study, an artificial neural network associated with image texture feature analysis is proposed to establish a predictable parameter model and automate the denoising procedure. In the proposed approach, a total of 83 image attributes were extracted based on four categories: 1) Basic image statistics. 2) Gray-level co-occurrence matrix (GLCM). 3) Gray-level run-length matrix (GLRLM) and 4) Tamura texture features. To obtain the ranking of discrimination in these texture features, a paired-samples t-test was applied to each individual image feature computed in every image. Subsequently, the sequential forward selection (SFS) method was used to select the best texture features according to the ranking of discrimination. The selected optimal features were further incorporated into a back propagation neural network to establish a predictable parameter model. A wide variety of MR images with various scenarios were adopted to evaluate the performance of the proposed framework. Experimental results indicated that this new automation system accurately predicted the bilateral filtering parameters and effectively removed the noise in a number of MR images. Comparing to the manually tuned filtering process, our approach not only produced better denoised results but also saved significant processing time. PMID:26405887

  20. Atypical charles bonnet syndrome.

    PubMed

    Arun, Priti; Jain, Rajan; Tripathi, Vaibhav

    2013-10-01

    Charles Bonnet syndrome (CBS) is not uncommon disorder. It may not present with all typical symptoms and intact insight. Here, a case of atypical CBS is reported where antipsychotics were not effective. Patient improved completely after restoration of vision.

  1. Atypical hematological response to combined calorie restriction and chronic hypoxia in Biosphere 2 crew: a possible link to latent features of hibernation capacity.

    PubMed

    Paglia, Donald E; Walford, Roy L

    2005-01-01

    Eight humans were isolated for 2 years in Biosphere 2, a sealed airtight habitat with recycled air, food, water, and wastes. A combination of conditions led to selective decline of oxygen (O2) in the internal atmosphere from 21% to 14%, inducing symptoms of high-altitude sickness but with little or no compensatory increase in red cell production. All crew members exhibited significant decreases in both erythrocyte 2,3-bisphosphoglycerate (2,3-BPG) concentrations and P50 [partial pressure of O2 for 50% hemoglobin (Hb) saturation] values, changes opposite those expected in adaptation to high-altitude hypoxia. Lower P50 with increased Hb-O2 affinity induced by low 2,3-BPG is a characteristic of hibernating species and could be advantageous in O2-impoverished environments. The mechanisms underlying these changes in the Biosphere 2 crew remain obscure but could be related to low-calorie diet (1750-2100 kcal/day). Because the combination of hypoxia and limited caloric intake is also characteristic of hibernation, this unusual response may represent a cross-adaptation phenomenon in which certain features of hibernation capability are expressed in humans.

  2. Novel radon transform-based method for linear feature detection in open water SAR images

    NASA Astrophysics Data System (ADS)

    Chen, Jie; Sun, Jiyin; Xu, Suqin; Chen, Biao

    2007-11-01

    Open water SAR images frequently exhibit long dark or bright linear features, some of which are ship wakes, internal wave or internal wave wakes of under water moving objects. The detection of these line features is very impotent in both civil and military fields. Considering to the drawbacks of conventional Radon transform, this paper proposed a novel liner feature detection method. It use the gliding window and firstly apply a Radon transform to the aim image, use a "mean matrix" to normalize the aim image in the Radon domain, and then search for the peaks or troughs in an ellipse region instead of the whole region. This algorithm is tested on a set of simulated SAR images of ship wakes. The results demonstrate that this algorithm's robustness in the presence of noise, as well as its ability to detect and localize linear features that are somewhat not so straight.

  3. Identification of natural images and computer-generated graphics based on statistical and textural features.

    PubMed

    Peng, Fei; Li, Jiao-ting; Long, Min

    2015-03-01

    To discriminate the acquisition pipelines of digital images, a novel scheme for the identification of natural images and computer-generated graphics is proposed based on statistical and textural features. First, the differences between them are investigated from the view of statistics and texture, and 31 dimensions of feature are acquired for identification. Then, LIBSVM is used for the classification. Finally, the experimental results are presented. The results show that it can achieve an identification accuracy of 97.89% for computer-generated graphics, and an identification accuracy of 97.75% for natural images. The analyses also demonstrate the proposed method has excellent performance, compared with some existing methods based only on statistical features or other features. The method has a great potential to be implemented for the identification of natural images and computer-generated graphics.

  4. Robust Low-Altitude Image Matching Based on Local Region Constraint and Feature Similarity Confidence

    NASA Astrophysics Data System (ADS)

    Chen, Min; Zhu, Qing; Huang, Shengzhi; Hu, Han; Wang, Jingxue

    2016-06-01

    Improving the matching reliability of low-altitude images is one of the most challenging issues in recent years, particularly for images with large viewpoint variation. In this study, an approach for low-altitude remote sensing image matching that is robust to the geometric transformation caused by viewpoint change is proposed. First, multiresolution local regions are extracted from the images and each local region is normalized to a circular area based on a transformation. Second, interest points are detected and clustered into local regions. The feature area of each interest point is determined under the constraint of the local region which the point belongs to. Then, a descriptor is computed for each interest point by using the classical scale invariant feature transform (SIFT). Finally, a feature matching strategy is proposed on the basis of feature similarity confidence to obtain reliable matches. Experimental results show that the proposed method provides significant improvements in the number of correct matches compared with other traditional methods.

  5. Glioma grading using cell nuclei morphologic features in digital pathology images

    NASA Astrophysics Data System (ADS)

    Reza, Syed M. S.; Iftekharuddin, Khan M.

    2016-03-01

    This work proposes a computationally efficient cell nuclei morphologic feature analysis technique to characterize the brain gliomas in tissue slide images. In this work, our contributions are two-fold: 1) obtain an optimized cell nuclei segmentation method based on the pros and cons of the existing techniques in literature, 2) extract representative features by k-mean clustering of nuclei morphologic features to include area, perimeter, eccentricity, and major axis length. This clustering based representative feature extraction avoids shortcomings of extensive tile [1] [2] and nuclear score [3] based methods for brain glioma grading in pathology images. Multilayer perceptron (MLP) is used to classify extracted features into two tumor types: glioblastoma multiforme (GBM) and low grade glioma (LGG). Quantitative scores such as precision, recall, and accuracy are obtained using 66 clinical patients' images from The Cancer Genome Atlas (TCGA) [4] dataset. On an average ~94% accuracy from 10 fold crossvalidation confirms the efficacy of the proposed method.

  6. Atypical autoerotic deaths

    SciTech Connect

    Gowitt, G.T.; Hanzlick, R.L. )

    1992-06-01

    So-called typical' autoerotic fatalities are the result of asphyxia due to mechanical compression of the neck, chest, or abdomen, whereas atypical' autoeroticism involves sexual self-stimulation by other means. The authors present five atypical autoerotic fatalities that involved the use of dichlorodifluoromethane, nitrous oxide, isobutyl nitrite, cocaine, or compounds containing 1-1-1-trichloroethane. Mechanisms of death are discussed in each case and the pertinent literature is reviewed.

  7. Local rigid registration for multimodal texture feature extraction from medical images

    NASA Astrophysics Data System (ADS)

    Steger, Sebastian

    2011-03-01

    The joint extraction of texture features from medical images of different modalities requires an accurate image registration at the target structures. In many cases rigid registration of the entire images does not achieve the desired accuracy whereas deformable registration is too complex and may result in undesired deformations. This paper presents a novel region of interest alignment approach based on local rigid registration enabling image fusion for multimodal texture feature extraction. First rigid registration on the entire images is performed to obtain an initial guess. Then small cubic regions around the target structure are clipped from all images and individually rigidly registered. The approach was applied to extract texture features in clinically acquired CT and MR images from lymph nodes in the oropharynx for an oral cancer reoccurrence prediction framework. Visual inspection showed that in all of the 30 cases at least a subtle misalignment was perceivable for the globally rigidly aligned images. After applying the presented approach the alignment of the target structure significantly improved in 19 cases. In 12 cases no alignment mismatch whatsoever was perceptible without requiring the complexity of deformable registration and without deforming the target structure. Further investigation showed that if the resolutions of the individual modalities differ significantly, partial volume effects occur, diminishing the significance of the multimodal features even for perfectly aligned images.

  8. Atypical Antipsychotics in the Treatment of Acute Bipolar Depression with Mixed Features: A Systematic Review and Exploratory Meta-Analysis of Placebo-Controlled Clinical Trials

    PubMed Central

    Fornaro, Michele; Stubbs, Brendon; De Berardis, Domenico; Perna, Giampaolo; Valchera, Alessandro; Veronese, Nicola; Solmi, Marco; Ganança, Licínia

    2016-01-01

    Evidence supporting the use of second generation antipsychotics (SGAs) in the treatment of acute depression with mixed features (MFs) associated with bipolar disorder (BD) is scarce and equivocal. Therefore, we conducted a systematic review and preliminary meta-analysis investigating SGAs in the treatment of acute BD depression with MFs. Two authors independently searched major electronic databases from 1990 until September 2015 for randomized (placebo-) controlled trials (RCTs) or open-label clinical trials investigating the efficacy of SGAs in the treatment of acute bipolar depression with MFs. A random-effect meta-analysis calculating the standardized mean difference (SMD) between SGA and placebo for the mean baseline to endpoint change in depression as well as manic symptoms score was computed based on 95% confidence intervals (CI). Six RCTs and one open-label placebo-controlled studies (including post-hoc reports) representing 1023 patients were included. Participants received either ziprasidone, olanzapine, lurasidone, quetiapine or asenapine for an average of 6.5 weeks across the included studies. Meta-analysis with Duval and Tweedie adjustment for publication bias demonstrated that SGA resulted in significant improvements of (hypo-)manic symptoms of bipolar mixed depression as assessed by the means of the total scores of the Young Mania Rating Scale (YMRS) (SMD −0.74, 95% CI −1.20 to −0.28, n SGA = 907, control = 652). Meta-analysis demonstrated that participants in receipt of SGA (n = 979) experienced a large improvement in the Montgomery–Åsberg Depression Rating Scale (MADRS) scores (SMD −1.08, 95% CI −1.35 to −0.81, p < 0.001) vs. placebo (n = 678). Publication and measurement biases and relative paucity of studies. Overall, SGAs appear to offer favorable improvements in MADRS and YMRS scores vs. placebo. Nevertheless, given the preliminary nature of the present report, additional original studies are required to allow more reliable and

  9. Image feature based GPS trace filtering for road network generation and road segmentation

    SciTech Connect

    Yuan, Jiangye; Cheriyadat, Anil M.

    2015-10-19

    We propose a new method to infer road networks from GPS trace data and accurately segment road regions in high-resolution aerial images. Unlike previous efforts that rely on GPS traces alone, we exploit image features to infer road networks from noisy trace data. The inferred road network is used to guide road segmentation. We show that the number of image segments spanned by the traces and the trace orientation validated with image features are important attributes for identifying GPS traces on road regions. Based on filtered traces , we construct road networks and integrate them with image features to segment road regions. Lastly, our experiments show that the proposed method produces more accurate road networks than the leading method that uses GPS traces alone, and also achieves high accuracy in segmenting road regions even with very noisy GPS data.

  10. Image feature based GPS trace filtering for road network generation and road segmentation

    DOE PAGESBeta

    Yuan, Jiangye; Cheriyadat, Anil M.

    2015-10-19

    We propose a new method to infer road networks from GPS trace data and accurately segment road regions in high-resolution aerial images. Unlike previous efforts that rely on GPS traces alone, we exploit image features to infer road networks from noisy trace data. The inferred road network is used to guide road segmentation. We show that the number of image segments spanned by the traces and the trace orientation validated with image features are important attributes for identifying GPS traces on road regions. Based on filtered traces , we construct road networks and integrate them with image features to segmentmore » road regions. Lastly, our experiments show that the proposed method produces more accurate road networks than the leading method that uses GPS traces alone, and also achieves high accuracy in segmenting road regions even with very noisy GPS data.« less

  11. Hybrid Discrete Wavelet Transform and Gabor Filter Banks Processing for Features Extraction from Biomedical Images

    PubMed Central

    Lahmiri, Salim; Boukadoum, Mounir

    2013-01-01

    A new methodology for automatic feature extraction from biomedical images and subsequent classification is presented. The approach exploits the spatial orientation of high-frequency textural features of the processed image as determined by a two-step process. First, the two-dimensional discrete wavelet transform (DWT) is applied to obtain the HH high-frequency subband image. Then, a Gabor filter bank is applied to the latter at different frequencies and spatial orientations to obtain new Gabor-filtered image whose entropy and uniformity are computed. Finally, the obtained statistics are fed to a support vector machine (SVM) binary classifier. The approach was validated on mammograms, retina, and brain magnetic resonance (MR) images. The obtained classification accuracies show better performance in comparison to common approaches that use only the DWT or Gabor filter banks for feature extraction. PMID:27006906

  12. Feature and Contrast Enhancement of Mammographic Image Based on Multiscale Analysis and Morphology

    PubMed Central

    Wu, Shibin; Xie, Yaoqin

    2013-01-01

    A new algorithm for feature and contrast enhancement of mammographic images is proposed in this paper. The approach bases on multiscale transform and mathematical morphology. First of all, the Laplacian Gaussian pyramid operator is applied to transform the mammography into different scale subband images. In addition, the detail or high frequency subimages are equalized by contrast limited adaptive histogram equalization (CLAHE) and low-pass subimages are processed by mathematical morphology. Finally, the enhanced image of feature and contrast is reconstructed from the Laplacian Gaussian pyramid coefficients modified at one or more levels by contrast limited adaptive histogram equalization and mathematical morphology, respectively. The enhanced image is processed by global nonlinear operator. The experimental results show that the presented algorithm is effective for feature and contrast enhancement of mammogram. The performance evaluation of the proposed algorithm is measured by contrast evaluation criterion for image, signal-noise-ratio (SNR), and contrast improvement index (CII). PMID:24416072

  13. Orientation Modeling for Amateur Cameras by Matching Image Line Features and Building Vector Data

    NASA Astrophysics Data System (ADS)

    Hung, C. H.; Chang, W. C.; Chen, L. C.

    2016-06-01

    With the popularity of geospatial applications, database updating is getting important due to the environmental changes over time. Imagery provides a lower cost and efficient way to update the database. Three dimensional objects can be measured by space intersection using conjugate image points and orientation parameters of cameras. However, precise orientation parameters of light amateur cameras are not always available due to their costliness and heaviness of precision GPS and IMU. To automatize data updating, the correspondence of object vector data and image may be built to improve the accuracy of direct georeferencing. This study contains four major parts, (1) back-projection of object vector data, (2) extraction of image feature lines, (3) object-image feature line matching, and (4) line-based orientation modeling. In order to construct the correspondence of features between an image and a building model, the building vector features were back-projected onto the image using the initial camera orientation from GPS and IMU. Image line features were extracted from the imagery. Afterwards, the matching procedure was done by assessing the similarity between the extracted image features and the back-projected ones. Then, the fourth part utilized line features in orientation modeling. The line-based orientation modeling was performed by the integration of line parametric equations into collinearity condition equations. The experiment data included images with 0.06 m resolution acquired by Canon EOS Mark 5D II camera on a Microdrones MD4-1000 UAV. Experimental results indicate that 2.1 pixel accuracy may be reached, which is equivalent to 0.12 m in the object space.

  14. Gender Classification From Face Images Using Mutual Information and Feature Fusion

    NASA Astrophysics Data System (ADS)

    Perez, Claudio; Tapia, Juan; Estévez, Pablo; Held, Claudio

    2012-01-01

    In this article we report a new method for gender classification from frontal face images using feature selection based on mutual information and fusion of features extracted from intensity, shape, texture, and from three different spatial scales. We compare the results of three different mutual information measures: minimum redundancy and maximal relevance (mRMR), normalized mutual information feature selection (NMIFS), and conditional mutual information feature selection (CMIFS). We also show that by fusing features extracted from six different methods we significantly improve the gender classification results relative to those previously published, yielding 99.13% of the gender classification rate on the FERET database.

  15. Reproducibility and Prognosis of Quantitative Features Extracted from CT Images12

    PubMed Central

    Balagurunathan, Yoganand; Gu, Yuhua; Wang, Hua; Kumar, Virendra; Grove, Olya; Hawkins, Sam; Kim, Jongphil; Goldgof, Dmitry B; Hall, Lawrence O; Gatenby, Robert A; Gillies, Robert J

    2014-01-01

    We study the reproducibility of quantitative imaging features that are used to describe tumor shape, size, and texture from computed tomography (CT) scans of non-small cell lung cancer (NSCLC). CT images are dependent on various scanning factors. We focus on characterizing image features that are reproducible in the presence of variations due to patient factors and segmentation methods. Thirty-two NSCLC nonenhanced lung CT scans were obtained from the Reference Image Database to Evaluate Response data set. The tumors were segmented using both manual (radiologist expert) and ensemble (software-automated) methods. A set of features (219 three-dimensional and 110 two-dimensional) was computed, and quantitative image features were statistically filtered to identify a subset of reproducible and nonredundant features. The variability in the repeated experiment was measured by the test-retest concordance correlation coefficient (CCCTreT). The natural range in the features, normalized to variance, was measured by the dynamic range (DR). In this study, there were 29 features across segmentation methods found with CCCTreT and DR ≥ 0.9 and R2Bet ≥ 0.95. These reproducible features were tested for predicting radiologist prognostic score; some texture features (run-length and Laws kernels) had an area under the curve of 0.9. The representative features were tested for their prognostic capabilities using an independent NSCLC data set (59 lung adenocarcinomas), where one of the texture features, run-length gray-level nonuniformity, was statistically significant in separating the samples into survival groups (P ≤ .046). PMID:24772210

  16. Improved medical image modality classification using a combination of visual and textual features.

    PubMed

    Dimitrovski, Ivica; Kocev, Dragi; Kitanovski, Ivan; Loskovska, Suzana; Džeroski, Sašo

    2015-01-01

    In this paper, we present the approach that we applied to the medical modality classification tasks at the ImageCLEF evaluation forum. More specifically, we used the modality classification databases from the ImageCLEF competitions in 2011, 2012 and 2013, described by four visual and one textual types of features, and combinations thereof. We used local binary patterns, color and edge directivity descriptors, fuzzy color and texture histogram and scale-invariant feature transform (and its variant opponentSIFT) as visual features and the standard bag-of-words textual representation coupled with TF-IDF weighting. The results from the extensive experimental evaluation identify the SIFT and opponentSIFT features as the best performing features for modality classification. Next, the low-level fusion of the visual features improves the predictive performance of the classifiers. This is because the different features are able to capture different aspects of an image, their combination offering a more complete representation of the visual content in an image. Moreover, adding textual features further increases the predictive performance. Finally, the results obtained with our approach are the best results reported on these databases so far. PMID:24997992

  17. Introduction: Feature Issue on Phantoms for the Performance Evaluation and Validation of Optical Medical Imaging Devices

    PubMed Central

    Hwang, Jeeseong; Ramella-Roman, Jessica C.; Nordstrom, Robert

    2012-01-01

    The editors introduce the Biomedical Optics Express feature issue on “Phantoms for the Performance Evaluation and Validation of Optical Medical Imaging Devices.” This topic was the focus of a technical workshop that was held on November 7–8, 2011, in Washington, D.C. The feature issue includes 13 contributions from workshop attendees. PMID:22741084

  18. On the selection of optimal feature region set for robust digital image watermarking.

    PubMed

    Tsai, Jen-Sheng; Huang, Win-Bin; Kuo, Yau-Hwang

    2011-03-01

    A novel feature region selection method for robust digital image watermarking is proposed in this paper. This method aims to select a nonoverlapping feature region set, which has the greatest robustness against various attacks and can preserve image quality as much as possible after watermarked. It first performs a simulated attacking procedure using some predefined attacks to evaluate the robustness of every candidate feature region. According to the evaluation results, it then adopts a track-with-pruning procedure to search a minimal primary feature set which can resist the most predefined attacks. In order to enhance its resistance to undefined attacks under the constraint of preserving image quality, the primary feature set is then extended by adding into some auxiliary feature regions. This work is formulated as a multidimensional knapsack problem and solved by a genetic algorithm based approach. The experimental results for StirMark attacks on some benchmark images support our expectation that the primary feature set can resist all the predefined attacks and its extension can enhance the robustness against undefined attacks. Comparing with some well-known feature-based methods, the proposed method exhibits better performance in robust digital watermarking.

  19. Atypical presentation of atypical mycobacteria in atypical diabetes

    PubMed Central

    Biswas, Sugata Narayan; Chakraborty, Partha Pratim; Satpathi, Partha Sarathi; Patra, Shinjan

    2016-01-01

    A 45-year-old, non-obese male presented with low-grade, remittent fever and a fluctuant swelling over the posterior aspect of his lower left flank. Laboratory tests revealed leukocytosis, raised ESR, hyperglycemia and raised HbA1C levels. Light microscopy of Ziehl–Neelsen-stained pus sample revealed numerous acid-fast bacilli. After 72 h of incubating aspirated pus in Löwenstein–Jensen media, non-pigmented, cream-colored colonies were observed, suggestive of rapid-growing atypical forms of mycobacteria. Polymerase chain reaction of isolated bacteria identified Mycobacterium chelonae as causative organism. Abdominal skiagram revealed extensive pancreatic intraductal calcifications suggestive of fibrocalculous pancreatic diabetes and lumbar vertebral body destruction with evidence of paravertebral abscess. The patient was prescribed a split-mixed insulin regimen, clarithromycin and ciprofloxacin with complete resolution of the subcutaneous abscess at 6 months. Diabetic patients are prone to infections. Mycobacteria, especially atypical ones, involving the spine and subcutaneous tissues have rarely been reported. PMID:27127641

  20. Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters

    PubMed Central

    GALAVIS, PAULINA E.; HOLLENSEN, CHRISTIAN; JALLOW, NGONEH; PALIWAL, BHUDATT; JERAJ, ROBERT

    2014-01-01

    Background Characterization of textural features (spatial distributions of image intensity levels) has been considered as a tool for automatic tumor segmentation. The purpose of this work is to study the variability of the textural features in PET images due to different acquisition modes and reconstruction parameters. Material and methods Twenty patients with solid tumors underwent PET/CT scans on a GE Discovery VCT scanner, 45–60 minutes post-injection of 10 mCi of [18F]FDG. Scans were acquired in both 2D and 3D modes. For each acquisition the raw PET data was reconstructed using five different reconstruction parameters. Lesions were segmented on a default image using the threshold of 40% of maximum SUV. Fifty different texture features were calculated inside the tumors. The range of variations of the features were calculated with respect to the average value. Results Fifty textural features were classified based on the range of variation in three categories: small, intermediate and large variability. Features with small variability (range ≤ 5%) were entropy-first order, energy, maximal correlation coefficient (second order feature) and low-gray level run emphasis (high-order feature). The features with intermediate variability (10% ≤ range ≤ 25%) were entropy-GLCM, sum entropy, high gray level run emphsis, gray level non-uniformity, small number emphasis, and entropy-NGL. Forty remaining features presented large variations (range > 30%). Conclusion Textural features such as entropy-first order, energy, maximal correlation coefficient, and low-gray level run emphasis exhibited small variations due to different acquisition modes and reconstruction parameters. Features with low level of variations are better candidates for reproducible tumor segmentation. Even though features such as contrast-NGTD, coarseness, homogeneity, and busyness have been previously used, our data indicated that these features presented large variations, therefore they could not be

  1. Image library approach to evaluating parametric uncertainty in metrology of isolated feature width

    NASA Astrophysics Data System (ADS)

    Potzick, James

    2009-03-01

    When measuring the width of an isolated line or space on a wafer or photomask, only the feature's image is measured, not the object itself. Often the largest contributors to measurement uncertainty are the uncertainties in the parameters which affect the image. Measurement repeatability is often smaller than the combined parametric uncertainty. An isolated feature's edges are far enough away from nearest edges of other features that its image does not change if this distance is increased (about 10 wavelengths in an optical microscope or exposure tool, or several effective-beam-widths in a SEM). When the leading and trailing edges of the same feature are not isolated from each other the metrology process becomes nonlinear. Isolated features may not be amenable to measurement by grating methods (e.g., scatterometry), and there is no hard lower limit to how small an isolated feature can be measured. There are several ways to infer the size of an isolated feature from its image in a microscope (SEM, AFM, optical,...), and they all require image modeling. Image modeling accounts for the influence of all of the parameters which can affect the image, and relates the apparent linewidth (in the image) to the true linewidth (on the object). The values of these parameters, however, have uncertainties and these uncertainties propagate through the model and lead to parametric uncertainty in the linewidth measurement, along with the scale factor uncertainty and the measurement repeatability. The combined measurement uncertainty is required in order to decide if the result is adequate for its intended purpose and to ascertain if it is consistent with other similar results. The parametric uncertainty for optical photomask measurements derived using an edge threshold approach has been described previously [1]; this paper describes an image library approach to this issue and shows results for optical photomask metrology over a linewidth and spacewidth range of 10 nm to 4 μm. The

  2. A semantic image retrieval approach between visual features and medical concepts

    NASA Astrophysics Data System (ADS)

    Li, Jin; Liang, Hong; Yang, Guangda; Feng, Yaoyu; Lv, Meichao

    2009-07-01

    In the medical domain, digital images are produced in ever-increasing quantities, which offer great opportunities for diagnostics, therapy and training. So how to manage these data and utilize them effectively and efficiently possess significant technical challenges. Thus, the technique of Content-based Medical Image Retrieval (CBMIR) emerges as the times require. However, current CBMIR is not sufficient to capture the semantic content of images. Accordingly, in this paper, an innovative approach for medical image knowledge representation and retrieval is proposed by focusing on the mapping modeling between visual feature and semantic concept. Firstly, the low-level fusion visual features are extracted based on statistical features. Secondly, a set of disjoint semantic tokens with appearance in medical images is selected to define a Visual and Medical Vocabulary. Thirdly, to narrow down the semantic gap and increase the retrieval efficiency, we investigate support vector machine (SVM) to associate low-level visual image features with their highlevel semantic. Experiments are conducted with a medical image DB consisting of 300 diverse medical images obtained from the Hei Longjiang Province Hospital. And the comparison of the retrieval results shows that the approach proposed in this paper is effective.

  3. Clinical and Imaging Features of a Congenital Midline Cervical Cleft in a Neonate: A Rare Anomaly

    PubMed Central

    Bawa, Pritish; Ibrahim, Zachary; Amodio, John

    2015-01-01

    Congenital midline cervical cleft (CMCC) is a rare congenital anomaly. CMCC and its complications and treatment have been well described in ENT, dermatology, and pediatric surgery literature. However, to our knowledge, the imaging work-up has not been reported in the literature thus far. We present a case of CMCC in a neonate with description of clinical presentation and imaging features. PMID:26078904

  4. Observing Behavior and Atypically Restricted Stimulus Control

    ERIC Educational Resources Information Center

    Dube, William V.; Dickson, Chata A.; Balsamo, Lyn M.; O'Donnell, Kristin Lombard; Tomanari, Gerson Y.; Farren, Kevin M.; Wheeler, Emily E.; McIlvane, William J.

    2010-01-01

    Restricted stimulus control refers to discrimination learning with atypical limitations in the range of controlling stimuli or stimulus features. In the study reported here, 4 normally capable individuals and 10 individuals with intellectual disabilities (ID) performed two-sample delayed matching to sample. Sample-stimulus observing was recorded…

  5. Comparisom of Wavelet-Based and Hht-Based Feature Extraction Methods for Hyperspectral Image Classification

    NASA Astrophysics Data System (ADS)

    Huang, X.-M.; Hsu, P.-H.

    2012-07-01

    Hyperspectral images, which contain rich and fine spectral information, can be used to identify surface objects and improve land use/cover classification accuracy. Due to the property of high dimensionality of hyperspectral data, traditional statistics-based classifiers cannot be directly used on such images with limited training samples. This problem is referred as "curse of dimensionality". The commonly used method to solve this problem is dimensionality reduction, and feature extraction is used to reduce the dimensionality of hyperspectral images more frequently. There are two types of feature extraction methods. The first type is based on statistical property of data. The other type is based on time-frequency analysis. In this study, the time-frequency analysis methods are used to extract the features for hyperspectral image classification. Firstly, it has been proven that wavelet-based feature extraction provide an effective tool for spectral feature extraction. On the other hand, Hilbert-Huang transform (HHT), a relative new time-frequency analysis tool, has been widely used in nonlinear and nonstationary data analysis. In this study, wavelet transform and HHT are implemented on the hyperspectral data for physical spectral analysis. Therefore, we can get a small number of salient features, reduce the dimensionality of hyperspectral images and keep the accuracy of classification results. An AVIRIS data set is used to test the performance of the proposed HHT-based feature extraction methods; then, the results are compared with wavelet-based feature extraction. According to the experiment results, HHT-based feature extraction methods are effective tools and the results are similar with wavelet-based feature extraction methods.

  6. The design and implementation of image query system based on color feature

    NASA Astrophysics Data System (ADS)

    Yao, Xu-Dong; Jia, Da-Chun; Li, Lin

    2013-07-01

    ASP.NET technology was used to construct the B/S mode image query system. The theory and technology of database design, color feature extraction from image, index and retrieval in the construction of the image repository were researched. The campus LAN and WAN environment were used to test the system. From the test results, the needs of user queries about related resources were achieved by system architecture design.

  7. Effective palette indexing for image compression using self-organization of Kohonen feature map.

    PubMed

    Pei, Soo-Chang; Chuang, Yu-Ting; Chuang, Wei-Hong

    2006-09-01

    The process of limited-color image compression usually involves color quantization followed by palette re-indexing. Palette re-indexing could improve the compression of color-indexed images, but it is still complicated and consumes extra time. Making use of the topology-preserving property of self-organizing Kohonen feature map, we can generate a fairly good color index table to achieve both high image quality and high compression, without re-indexing. Promising experiment results will be presented.

  8. A unified framework of image latent feature learning on Sina microblog

    NASA Astrophysics Data System (ADS)

    Wei, Jinjin; Jin, Zhigang; Zhou, Yuan; Zhang, Rui

    2015-10-01

    Large-scale user-contributed images with texts are rapidly increasing on the social media websites, such as Sina microblog. However, the noise and incomplete correspondence between the images and the texts give rise to the difficulty in precise image retrieval and ranking. In this paper, a hypergraph-based learning framework is proposed for image ranking, which simultaneously utilizes visual feature, textual content and social link information to estimate the relevance between images. Representing each image as a vertex in the hypergraph, complex relationship between images can be reflected exactly. Then updating the weight of hyperedges throughout the hypergraph learning process, the effect of different edges can be adaptively modulated in the constructed hypergraph. Furthermore, the popularity degree of the image is employed to re-rank the retrieval results. Comparative experiments on a large-scale Sina microblog data-set demonstrate the effectiveness of the proposed approach.

  9. Imaging nanoscale features with plasmon-coupled leakage radiation far-field superlenses.

    PubMed

    Regan, Charles J; Rodriguez, Robier; Gourshetty, Shivkumar C; Grave de Peralta, Luis; Bernussi, Ayrton A

    2012-09-10

    Optical images from nano-scale features were obtained by collection of leakage radiation coupled to surface plasmon polaritons excited by near-field fluorescence. Plasmonic crystals with spatial periods as small as 190 nm and non-periodic features separated by 80 nm, corresponding to ~λ/7, were clearly visible in the real plane images using this far-field technique. We show that the leaked light from the investigated samples carries detailed information to the far-field which is not present in the images obtained with conventional optical microscopy.

  10. 3D-2D Deformable Image Registration Using Feature-Based Nonuniform Meshes.

    PubMed

    Zhong, Zichun; Guo, Xiaohu; Cai, Yiqi; Yang, Yin; Wang, Jing; Jia, Xun; Mao, Weihua

    2016-01-01

    By using prior information of planning CT images and feature-based nonuniform meshes, this paper demonstrates that volumetric images can be efficiently registered with a very small portion of 2D projection images of a Cone-Beam Computed Tomography (CBCT) scan. After a density field is computed based on the extracted feature edges from planning CT images, nonuniform tetrahedral meshes will be automatically generated to better characterize the image features according to the density field; that is, finer meshes are generated for features. The displacement vector fields (DVFs) are specified at the mesh vertices to drive the deformation of original CT images. Digitally reconstructed radiographs (DRRs) of the deformed anatomy are generated and compared with corresponding 2D projections. DVFs are optimized to minimize the objective function including differences between DRRs and projections and the regularity. To further accelerate the above 3D-2D registration, a procedure to obtain good initial deformations by deforming the volume surface to match 2D body boundary on projections has been developed. This complete method is evaluated quantitatively by using several digital phantoms and data from head and neck cancer patients. The feature-based nonuniform meshing method leads to better results than either uniform orthogonal grid or uniform tetrahedral meshes. PMID:27019849

  11. 3D-2D Deformable Image Registration Using Feature-Based Nonuniform Meshes

    PubMed Central

    Guo, Xiaohu; Cai, Yiqi; Yang, Yin; Wang, Jing; Jia, Xun

    2016-01-01

    By using prior information of planning CT images and feature-based nonuniform meshes, this paper demonstrates that volumetric images can be efficiently registered with a very small portion of 2D projection images of a Cone-Beam Computed Tomography (CBCT) scan. After a density field is computed based on the extracted feature edges from planning CT images, nonuniform tetrahedral meshes will be automatically generated to better characterize the image features according to the density field; that is, finer meshes are generated for features. The displacement vector fields (DVFs) are specified at the mesh vertices to drive the deformation of original CT images. Digitally reconstructed radiographs (DRRs) of the deformed anatomy are generated and compared with corresponding 2D projections. DVFs are optimized to minimize the objective function including differences between DRRs and projections and the regularity. To further accelerate the above 3D-2D registration, a procedure to obtain good initial deformations by deforming the volume surface to match 2D body boundary on projections has been developed. This complete method is evaluated quantitatively by using several digital phantoms and data from head and neck cancer patients. The feature-based nonuniform meshing method leads to better results than either uniform orthogonal grid or uniform tetrahedral meshes. PMID:27019849

  12. Vehicle detection from high-resolution aerial images based on superpixel and color name features

    NASA Astrophysics Data System (ADS)

    Chen, Ziyi; Cao, Liujuan; Yu, Zang; Chen, Yiping; Wang, Cheng; Li, Jonathan

    2016-03-01

    Automatic vehicle detection from aerial images is emerging due to the strong demand of large-area traffic monitoring. In this paper, we present a novel framework for automatic vehicle detection from the aerial images. Through superpixel segmentation, we first segment the aerial images into homogeneous patches, which consist of the basic units during the detection to improve efficiency. By introducing the sparse representation into our method, powerful classification ability is achieved after the dictionary training. To effectively describe a patch, the Histogram of Oriented Gradient (HOG) is used. We further propose to integrate color information to enrich the feature representation by using the color name feature. The final feature consists of both HOG and color name based histogram, by which we get a strong descriptor of a patch. Experimental results demonstrate the effectiveness and robust performance of the proposed algorithm for vehicle detection from aerial images.

  13. Computer-aided breast MR image feature analysis for prediction of tumor response to chemotherapy

    SciTech Connect

    Aghaei, Faranak; Tan, Maxine; Liu, Hong; Zheng, Bin; Hollingsworth, Alan B.; Qian, Wei

    2015-11-15

    Purpose: To identify a new clinical marker based on quantitative kinetic image features analysis and assess its feasibility to predict tumor response to neoadjuvant chemotherapy. Methods: The authors assembled a dataset involving breast MR images acquired from 68 cancer patients before undergoing neoadjuvant chemotherapy. Among them, 25 patients had complete response (CR) and 43 had partial and nonresponse (NR) to chemotherapy based on the response evaluation criteria in solid tumors. The authors developed a computer-aided detection scheme to segment breast areas and tumors depicted on the breast MR images and computed a total of 39 kinetic image features from both tumor and background parenchymal enhancement regions. The authors then applied and tested two approaches to classify between CR and NR cases. The first one analyzed each individual feature and applied a simple feature fusion method that combines classification results from multiple features. The second approach tested an attribute selected classifier that integrates an artificial neural network (ANN) with a wrapper subset evaluator, which was optimized using a leave-one-case-out validation method. Results: In the pool of 39 features, 10 yielded relatively higher classification performance with the areas under receiver operating characteristic curves (AUCs) ranging from 0.61 to 0.78 to classify between CR and NR cases. Using a feature fusion method, the maximum AUC = 0.85 ± 0.05. Using the ANN-based classifier, AUC value significantly increased to 0.96 ± 0.03 (p < 0.01). Conclusions: This study demonstrated that quantitative analysis of kinetic image features computed from breast MR images acquired prechemotherapy has potential to generate a useful clinical marker in predicting tumor response to chemotherapy.

  14. Computer-aided breast MR image feature analysis for prediction of tumor response to chemotherapy

    PubMed Central

    Aghaei, Faranak; Tan, Maxine; Hollingsworth, Alan B.; Qian, Wei; Liu, Hong; Zheng, Bin

    2015-01-01

    Purpose: To identify a new clinical marker based on quantitative kinetic image features analysis and assess its feasibility to predict tumor response to neoadjuvant chemotherapy. Methods: The authors assembled a dataset involving breast MR images acquired from 68 cancer patients before undergoing neoadjuvant chemotherapy. Among them, 25 patients had complete response (CR) and 43 had partial and nonresponse (NR) to chemotherapy based on the response evaluation criteria in solid tumors. The authors developed a computer-aided detection scheme to segment breast areas and tumors depicted on the breast MR images and computed a total of 39 kinetic image features from both tumor and background parenchymal enhancement regions. The authors then applied and tested two approaches to classify between CR and NR cases. The first one analyzed each individual feature and applied a simple feature fusion method that combines classification results from multiple features. The second approach tested an attribute selected classifier that integrates an artificial neural network (ANN) with a wrapper subset evaluator, which was optimized using a leave-one-case-out validation method. Results: In the pool of 39 features, 10 yielded relatively higher classification performance with the areas under receiver operating characteristic curves (AUCs) ranging from 0.61 to 0.78 to classify between CR and NR cases. Using a feature fusion method, the maximum AUC = 0.85 ± 0.05. Using the ANN-based classifier, AUC value significantly increased to 0.96 ± 0.03 (p < 0.01). Conclusions: This study demonstrated that quantitative analysis of kinetic image features computed from breast MR images acquired prechemotherapy has potential to generate a useful clinical marker in predicting tumor response to chemotherapy. PMID:26520742

  15. Feature-based registration of historical aerial images by Area Minimization

    NASA Astrophysics Data System (ADS)

    Nagarajan, Sudhagar; Schenk, Toni

    2016-06-01

    The registration of historical images plays a significant role in assessing changes in land topography over time. By comparing historical aerial images with recent data, geometric changes that have taken place over the years can be quantified. However, the lack of ground control information and precise camera parameters has limited scientists' ability to reliably incorporate historical images into change detection studies. Other limitations include the methods of determining identical points between recent and historical images, which has proven to be a cumbersome task due to continuous land cover changes. Our research demonstrates a method of registering historical images using Time Invariant Line (TIL) features. TIL features are different representations of the same line features in multi-temporal data without explicit point-to-point or straight line-to-straight line correspondence. We successfully determined the exterior orientation of historical images by minimizing the area formed between corresponding TIL features in recent and historical images. We then tested the feasibility of the approach with synthetic and real data and analyzed the results. Based on our analysis, this method shows promise for long-term 3D change detection studies.

  16. Mine detection using variational methods for image enhancement and feature extraction

    NASA Astrophysics Data System (ADS)

    Szymczak, William G.; Guo, Weiming; Rogers, Joel Clark W.

    1998-09-01

    A critical part of automatic classification algorithms is the extraction of features which distinguish targets from background noise and clutter. The focus of this paper is the use of variational methods for improving the classification of sea mines from both side-scan sonar and laser line-scan images. These methods are based on minimizing a functional of the image intensity. Examples include Total Variation Minimization (TVM) which is very effective for reducing the noise of an image without compromising its edge features, and Mumford-Shah segmentation, which in its simplest form, provides an optimal piecewise constant partition of the image. For the sonar side-scan images it is shown that a combination of these two variational methods, (first reducing the noise using TVM, then using segmentation) outperforms the use of either one individually for the extraction of minelike features. Multichannel segmentation based on a wavelet decomposition is also effectively used to declutter a sonar image. Finally, feature extraction and classification using segmentation is demonstrated on laser line-scan images of mines in a cluttered sea floor.

  17. Atypical parkinsonism: an update

    PubMed Central

    Stamelou, Maria; Hoeglinger, Guenter U.

    2013-01-01

    Purpose of review This update discusses novel aspects on genetics, diagnosis, and treatments of atypical parkinsonism published over the past 2 years. Recent findings A genome-wide association study identified new genetic risk factors for progressive supranuclear palsy and new genetic conditions presenting with atypical parkinsonism have been described. The clinical criteria for diagnosis of corticobasal degeneration have been revised, and for progressive supranuclear palsy are under revision. Novel molecular techniques to identify possible biomarkers, as in other neurodegenerative disorders, have started being studied on atypical parkinsonian conditions, and although preliminary results seem promising, further studies are urgently warranted. Therapeutic trials based on disease-specific targets have shown no clinical improvement. Summary The knowledge obtained recently on atypical parkinsonian conditions points out the major deficits in this field. With the expanding phenotypical spectrum of atypical parkinsonian conditions, the early identification of patients has become difficult. The inability of conventional methods to identify these disorders earlier and better than clinicians, and the recent failure of promising therapeutic compounds, highlight the fact that the lack of biomarkers is probably the greatest limitation for developing treatments for these disorders. Thus, current and future research in this direction will be crucial. PMID:23812308

  18. Feature extraction and segmentation in medical images by statistical optimization and point operation approaches

    NASA Astrophysics Data System (ADS)

    Yang, Shuyu; King, Philip; Corona, Enrique; Wilson, Mark P.; Aydin, Kaan; Mitra, Sunanda; Soliz, Peter; Nutter, Brian S.; Kwon, Young H.

    2003-05-01

    Feature extraction is a critical preprocessing step, which influences the outcome of the entire process of developing significant metrics for medical image evaluation. The purpose of this paper is firstly to compare the effect of an optimized statistical feature extraction methodology to a well designed combination of point operations for feature extraction at the preprocessing stage of retinal images for developing useful diagnostic metrics for retinal diseases such as glaucoma and diabetic retinopathy. Segmentation of the extracted features allow us to investigate the effect of occlusion induced by these features on generating stereo disparity mapping and 3-D visualization of the optic cup/disc. Segmentation of blood vessels in the retina also has significant application in generating precise vessel diameter metrics in vascular diseases such as hypertension and diabetic retinopathy for monitoring progression of retinal diseases.

  19. Partial dependence of breast tumor malignancy on ultrasound image features derived from boosted trees

    NASA Astrophysics Data System (ADS)

    Yang, Wei; Zhang, Su; Li, Wenying; Chen, Yaqing; Lu, Hongtao; Chen, Wufan; Chen, Yazhu

    2010-04-01

    Various computerized features extracted from breast ultrasound images are useful in assessing the malignancy of breast tumors. However, the underlying relationship between the computerized features and tumor malignancy may not be linear in nature. We use the decision tree ensemble trained by the cost-sensitive boosting algorithm to approximate the target function for malignancy assessment and to reflect this relationship qualitatively. Partial dependence plots are employed to explore and visualize the effect of features on the output of the decision tree ensemble. In the experiments, 31 image features are extracted to quantify the sonographic characteristics of breast tumors. Patient age is used as an external feature because of its high clinical importance. The area under the receiver-operating characteristic curve of the tree ensembles can reach 0.95 with sensitivity of 0.95 (61/64) at the associated specificity 0.74 (77/104). The partial dependence plots of the four most important features are demonstrated to show the influence of the features on malignancy, and they are in accord with the empirical observations. The results can provide visual and qualitative references on the computerized image features for physicians, and can be useful for enhancing the interpretability of computer-aided diagnosis systems for breast ultrasound.

  20. Atypical periorbital xanthogranulomas associated with systemic benign lymphoepithelial lesions.

    PubMed

    Butterfield, J H; Bartley, G B

    1994-12-01

    The report reviews the clinical, laboratory, and pathologic features of a 47-year-old woman with systemic benign lymphoepithelial lesions in whom atypical periorbital xanthogranulomas with rare central necrosis subsequently developed.

  1. Image Generation Using Bidirectional Integral Features for Face Recognition with a Single Sample per Person

    PubMed Central

    Lee, Yonggeol; Lee, Minsik; Choi, Sang-Il

    2015-01-01

    In face recognition, most appearance-based methods require several images of each person to construct the feature space for recognition. However, in the real world it is difficult to collect multiple images per person, and in many cases there is only a single sample per person (SSPP). In this paper, we propose a method to generate new images with various illuminations from a single image taken under frontal illumination. Motivated by the integral image, which was developed for face detection, we extract the bidirectional integral feature (BIF) to obtain the characteristics of the illumination condition at the time of the picture being taken. The experimental results for various face databases show that the proposed method results in improved recognition performance under illumination variation. PMID:26414018

  2. Learning representative features for facial images based on a modified principal component analysis

    NASA Astrophysics Data System (ADS)

    Averkin, Anton; Potapov, Alexey

    2013-05-01

    The paper is devoted to facial image analysis and particularly deals with the problem of automatic evaluation of the attractiveness of human faces. We propose a new approach for automatic construction of feature space based on a modified principal component analysis. Input data sets for the algorithm are the learning data sets of facial images, which are rated by one person. The proposed approach allows one to extract features of the individual subjective face beauty perception and to predict attractiveness values for new facial images, which were not included into a learning data set. The Pearson correlation coefficient between values predicted by our method for new facial images and personal attractiveness estimation values equals to 0.89. This means that the new approach proposed is promising and can be used for predicting subjective face attractiveness values in real systems of the facial images analysis.

  3. Lumbar Ultrasound Image Feature Extraction and Classification with Support Vector Machine.

    PubMed

    Yu, Shuang; Tan, Kok Kiong; Sng, Ban Leong; Li, Shengjin; Sia, Alex Tiong Heng

    2015-10-01

    Needle entry site localization remains a challenge for procedures that involve lumbar puncture, for example, epidural anesthesia. To solve the problem, we have developed an image classification algorithm that can automatically identify the bone/interspinous region for ultrasound images obtained from lumbar spine of pregnant patients in the transverse plane. The proposed algorithm consists of feature extraction, feature selection and machine learning procedures. A set of features, including matching values, positions and the appearance of black pixels within pre-defined windows along the midline, were extracted from the ultrasound images using template matching and midline detection methods. A support vector machine was then used to classify the bone images and interspinous images. The support vector machine model was trained with 1,040 images from 26 pregnant subjects and tested on 800 images from a separate set of 20 pregnant patients. A success rate of 95.0% on training set and 93.2% on test set was achieved with the proposed method. The trained support vector machine model was further tested on 46 off-line collected videos, and successfully identified the proper needle insertion site (interspinous region) in 45 of the cases. Therefore, the proposed method is able to process the ultrasound images of lumbar spine in an automatic manner, so as to facilitate the anesthetists' work of identifying the needle entry site.

  4. Rough-Fuzzy Clustering and Unsupervised Feature Selection for Wavelet Based MR Image Segmentation

    PubMed Central

    Maji, Pradipta; Roy, Shaswati

    2015-01-01

    Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of brain magnetic resonance (MR) images. For many human experts, manual segmentation is a difficult and time consuming task, which makes an automated brain MR image segmentation method desirable. In this regard, this paper presents a new segmentation method for brain MR images, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique. The proposed method assumes that the major brain tissues, namely, gray matter, white matter, and cerebrospinal fluid from the MR images are considered to have different textural properties. The dyadic wavelet analysis is used to extract the scale-space feature vector for each pixel, while the rough-fuzzy clustering is used to address the uncertainty problem of brain MR image segmentation. An unsupervised feature selection method is introduced, based on maximum relevance-maximum significance criterion, to select relevant and significant textural features for segmentation problem, while the mathematical morphology based skull stripping preprocessing step is proposed to remove the non-cerebral tissues like skull. The performance of the proposed method, along with a comparison with related approaches, is demonstrated on a set of synthetic and real brain MR images using standard validity indices. PMID:25848961

  5. Asymmetries in the direction of saccades during perception of scenes and fractals: effects of image type and image features.

    PubMed

    Foulsham, Tom; Kingstone, Alan

    2010-04-01

    The direction in which people tend to move their eyes when inspecting images can reveal the different influences on eye guidance in scene perception, and their time course. We investigated biases in saccade direction during a memory-encoding task with natural scenes and computer-generated fractals. Images were rotated to disentangle egocentric and image-based guidance. Saccades in fractals were more likely to be horizontal, regardless of orientation. In scenes, the first saccade often moved down and subsequent eye movements were predominantly vertical, relative to the scene. These biases were modulated by the distribution of visual features (saliency and clutter) in the scene. The results suggest that image orientation, visual features and the scene frame-of-reference have a rapid effect on eye guidance.

  6. New method for identifying features of an image on a digital video display

    NASA Astrophysics Data System (ADS)

    Doyle, Michael D.

    1991-04-01

    The MetaMap process extends the concept of direct manipulation human-computer interfaces to new limits. Its specific capabilities include the correlation of discrete image elements to relevant text information and the correlation of these image features to other images as well as to program control mechanisms. The correlation is accomplished through reprogramming of both the color map and the image so that discrete image elements comprise unique sets of color indices. This process allows the correlation to be accomplished with very efficient data storage and program execution times. Image databases adapted to this process become object-oriented as a result. Very sophisticated interrelationships can be set up between images text and program control mechanisms using this process. An application of this interfacing process to the design of an interactive atlas of medical histology as well as other possible applications are described. The MetaMap process is protected by U. S. patent #4

  7. CT image noise reduction using rotational-invariant feature in Stockwell transform

    NASA Astrophysics Data System (ADS)

    Su, Jian; Li, Zhoubo; Yu, Lifeng; Warner, Joshua; Blezek, Daniel; Erickson, Bradley

    2014-03-01

    Iterative reconstruction and other noise reduction methods have been employed in CT to improve image quality and to reduce radiation dose. The non-local means (NLM) filter emerges as a popular choice for image-based noise reduction in CT. However, the original NLM method cannot incorporate similar structures if they are in a rotational format, resulting in ineffective denoising in some locations of the image and non-uniform noise reduction across the image. We have developed a novel rotational-invariant image texture feature derived from the multiresolutional Stockwell-transform (ST), and applied it to CT image noise reduction so that similar structures can be identified and fully utilized even when they are in different orientations. We performed a computer simulation study in CT to demonstrate better efficiency in terms of utilizing redundant information in the image and more uniform noise reduction achieved by ST than by NLM.

  8. Computer-aided diagnostic method for classification of Alzheimer's disease with atrophic image features on MR images

    NASA Astrophysics Data System (ADS)

    Arimura, Hidetaka; Yoshiura, Takashi; Kumazawa, Seiji; Tanaka, Kazuhiro; Koga, Hiroshi; Mihara, Futoshi; Honda, Hiroshi; Sakai, Shuji; Toyofuku, Fukai; Higashida, Yoshiharu

    2008-03-01

    Our goal for this study was to attempt to develop a computer-aided diagnostic (CAD) method for classification of Alzheimer's disease (AD) with atrophic image features derived from specific anatomical regions in three-dimensional (3-D) T1-weighted magnetic resonance (MR) images. Specific regions related to the cerebral atrophy of AD were white matter and gray matter regions, and CSF regions in this study. Cerebral cortical gray matter regions were determined by extracting a brain and white matter regions based on a level set based method, whose speed function depended on gradient vectors in an original image and pixel values in grown regions. The CSF regions in cerebral sulci and lateral ventricles were extracted by wrapping the brain tightly with a zero level set determined from a level set function. Volumes of the specific regions and the cortical thickness were determined as atrophic image features. Average cortical thickness was calculated in 32 subregions, which were obtained by dividing each brain region. Finally, AD patients were classified by using a support vector machine, which was trained by the image features of AD and non-AD cases. We applied our CAD method to MR images of whole brains obtained from 29 clinically diagnosed AD cases and 25 non-AD cases. As a result, the area under a receiver operating characteristic (ROC) curve obtained by our computerized method was 0.901 based on a leave-one-out test in identification of AD cases among 54 cases including 8 AD patients at early stages. The accuracy for discrimination between 29 AD patients and 25 non-AD subjects was 0.840, which was determined at the point where the sensitivity was the same as the specificity on the ROC curve. This result showed that our CAD method based on atrophic image features may be promising for detecting AD patients by using 3-D MR images.

  9. [Determination of Soluble Solid Content in Strawberry Using Hyperspectral Imaging Combined with Feature Extraction Methods].

    PubMed

    Ding, Xi-bin; Zhang, Chu; Liu, Fei; Song, Xing-lin; Kong, Wen-wen; He, Yong

    2015-04-01

    Hyperspectral imaging combined with feature extraction methods were applied to determine soluble sugar content (SSC) in mature and scatheless strawberry. Hyperspectral images of 154 strawberries covering the spectral range of 874-1,734 nm were captured and the spectral data were extracted from the hyperspectral images, and the spectra of 941~1,612 nm were preprocessed by moving average (MA). Nineteen samples were defined as outliers by the residual method, and the remaining 135 samples were divided into the calibration set (n = 90) and the prediction set (n = 45). Successive projections algorithm (SPA), genetic algorithm partial least squares (GAPLS) combined with SPA, weighted regression coefficient (Bw) and competitive adaptive reweighted sampling (CARS) were applied to select 14, 17, 24 and 25 effective wavelengths, respectively. Principal component analysis (PCA) and wavelet transform (WT) were applied to extract feature information with 20 and 58 features, respectively. PLS models were built based on the full spectra, the effective wavelengths and the features, respectively. All PLS models obtained good results. PLS models using full-spectra and features extracted by WT obtained the best results with correlation coefficient of calibration (r(c)) and correlation coefficient of prediction (r(p)) over 0.9. The overall results indicated that hyperspectral imaging combined with feature extraction methods could be used for detection of SSC in strawberry. PMID:26197594

  10. Automated, Computerized, Feature-Based Phenotype Analysis of Slit Lamp Images of the Mouse Lens

    PubMed Central

    Yuen, Jenny; Li, Yi; Shapiro, Linda G.; Clark, John I.; Arnett, Ernest; Sage, E. Helene; Brinkley, James F.

    2008-01-01

    Longitudinal studies of a variety of transgenic mouse models for lens development can create substantial challenges in database management and analysis. We report a novel, automated, feature-based informatics approach to screening lens phenotypes in a large database of slit lamp images. Digital slit lamp images of normal and abnormal lenses in eyes of wild type (wt), SC1 null and SPARC null transgenic mice were recorded for quantitative evaluation of their structural phenotype. The images were processed to improve the contrast of structural features that corresponded to rings of opacity and fluctuations in scattering intensity in the lenses. Measurable attributes were assigned to the features in the lens images and given as an output vector of 46 dimensions. Characteristic patterns correlated with the structural phenotype of each mutant and wt lens and a statistical fit for each phenotype was defined. The genotype was identified correctly in nearly 85% of the slit lamp images on the basis of an automated computer analysis of the lens structural phenotype. The automated computer algorithm has the potential to evaluate a large database of slit lamp images and distinguish mouse genotypes on the basis of lens phenotypes objectively using a neural network analysis of the structural features observed in the slit lamp images. The neural network approach is a promising technology for objective evaluation of genotype/phenotype relationships based on structural features and light scattering in lenses. Further improvements in the automated method can be expected to simplify and increase the accuracy and efficiency of the feature based analysis of structural phenotypes linked to genetic variation. PMID:18304532

  11. A unified framework for image retrieval using keyword and visual features.

    PubMed

    Jing, Feng; Li, Mingling; Zhang, Hong-Jiang; Zhang, Bo

    2005-07-01

    In this paper, a unified image retrieval framework based on both keyword annotations and visual features is proposed. In this framework, a set of statistical models are built based on visual features of a small set of manually labeled images to represent semantic concepts and used to propagate keywords to other unlabeled images. These models are updated periodically when more images implicitly labeled by users become available through relevance feedback. In this sense, the keyword models serve the function of accumulation and memorization of knowledge learned from user-provided relevance feedback. Furthermore, two sets of effective and efficient similarity measures and relevance feedback schemes are proposed for query by keyword scenario and query by image example scenario, respectively. Keyword models are combined with visual features in these schemes. In particular, a new, entropy-based active learning strategy is introduced to improve the efficiency of relevance feedback for query by keyword. Furthermore, a new algorithm is proposed to estimate the keyword features of the search concept for query by image example. It is shown to be more appropriate than two existing relevance feedback algorithms. Experimental results demonstrate the effectiveness of the proposed framework.

  12. Atypical Hemolytic Uremic Syndrome

    PubMed Central

    Kavanagh, David; Goodship, Tim H.; Richards, Anna

    2013-01-01

    Summary Hemolytic uremic syndrome (HUS) is a triad of microangiopathic hemolytic anemia, thrombocytopenia, and acute renal failure. The atypical form of HUS is a disease characterized by complement overactivation. Inherited defects in complement genes and acquired autoantibodies against complement regulatory proteins have been described. Incomplete penetrance of mutations in all predisposing genes is reported, suggesting that a precipitating event or trigger is required to unmask the complement regulatory deficiency. The underlying genetic defect predicts the prognosis both in native kidneys and after renal transplantation. The successful trials of the complement inhibitor eculizumab in the treatment of atypical HUS will revolutionize disease management. PMID:24161037

  13. Atypical pityriasis rosea.

    PubMed

    Imamura, S; Ozaki, M; Oguchi, M; Okamoto, H; Horiguchi, Y

    1985-01-01

    Six cases of pityriasis rosea with atypical morphology and distribution of the eruption are reported. The eruption did not show a typical 'Christmas-tree' arrangement, confined to the trunk and proximal parts of the extremities. However, the histology of the eruption revealed dyskeratotic cells in the epidermis and extravasated erythrocytes in the dermis, which were recently reported as rather characteristic findings of this disease. Prodromal symptoms, course and response to therapy were compatible with pityriasis rosea. Histological examination is important and helpful for the diagnosis of atypical cases.

  14. Automatic Ship Detection in Single-Pol SAR Images Using Texture Features in Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Khesali, E.; Enayati, H.; Modiri, M.; Mohseni Aref, M.

    2015-12-01

    This paper presents a novel method for detecting ships from high-resolution synthetic aperture radar (SAR) images. This method categorizes ship targets from single-pol SAR images using texture features in artificial neural networks. As such, the method tries to overcome the lack of an operational solution that is able to reliably detect ships with one SAR channel. The method has the following three main stages: 1) feature extraction; 2) feature selection; and 3) ship detection. The first part extracts different texture features from SAR image. These textures include occurrence and co occurrence measures with different window sizes. Then, best features are selected. Finally, the artificial neural network is used to extract ship pixels from sea ones. In post processing stage some morphological filters are used to improve the result. The effectiveness of the proposed method is verified using Sentinel-1 data in VV polarization. Experimental results indicate that the proposed algorithm can be implemented with time-saving, high precision ship extraction, feature analysis, and detection. The results also show that using texture features the algorithm properly discriminates speckle noise from ships.

  15. Study on image feature extraction and classification for human colorectal cancer using optical coherence tomography

    NASA Astrophysics Data System (ADS)

    Huang, Shu-Wei; Yang, Shan-Yi; Huang, Wei-Cheng; Chiu, Han-Mo; Lu, Chih-Wei

    2011-06-01

    Most of the colorectal cancer has grown from the adenomatous polyp. Adenomatous lesions have a well-documented relationship to colorectal cancer in previous studies. Thus, to detect the morphological changes between polyp and tumor can allow early diagnosis of colorectal cancer and simultaneous removal of lesions. OCT (Optical coherence tomography) has been several advantages including high resolution and non-invasive cross-sectional image in vivo. In this study, we investigated the relationship between the B-scan OCT image features and histology of malignant human colorectal tissues, also en-face OCT image and the endoscopic image pattern. The in-vitro experiments were performed by a swept-source optical coherence tomography (SS-OCT) system; the swept source has a center wavelength at 1310 nm and 160nm in wavelength scanning range which produced 6 um axial resolution. In the study, the en-face images were reconstructed by integrating the axial values in 3D OCT images. The reconstructed en-face images show the same roundish or gyrus-like pattern with endoscopy images. The pattern of en-face images relate to the stages of colon cancer. Endoscopic OCT technique would provide three-dimensional imaging and rapidly reconstruct en-face images which can increase the speed of colon cancer diagnosis. Our results indicate a great potential for early detection of colorectal adenomas by using the OCT imaging.

  16. Face Recognition for Access Control Systems Combining Image-Difference Features Based on a Probabilistic Model

    NASA Astrophysics Data System (ADS)

    Miwa, Shotaro; Kage, Hiroshi; Hirai, Takashi; Sumi, Kazuhiko

    We propose a probabilistic face recognition algorithm for Access Control System(ACS)s. Comparing with existing ACSs using low cost IC-cards, face recognition has advantages in usability and security that it doesn't require people to hold cards over scanners and doesn't accept imposters with authorized cards. Therefore face recognition attracts more interests in security markets than IC-cards. But in security markets where low cost ACSs exist, price competition is important, and there is a limitation on the quality of available cameras and image control. Therefore ACSs using face recognition are required to handle much lower quality images, such as defocused and poor gain-controlled images than high security systems, such as immigration control. To tackle with such image quality problems we developed a face recognition algorithm based on a probabilistic model which combines a variety of image-difference features trained by Real AdaBoost with their prior probability distributions. It enables to evaluate and utilize only reliable features among trained ones during each authentication, and achieve high recognition performance rates. The field evaluation using a pseudo Access Control System installed in our office shows that the proposed system achieves a constant high recognition performance rate independent on face image qualities, that is about four times lower EER (Equal Error Rate) under a variety of image conditions than one without any prior probability distributions. On the other hand using image difference features without any prior probabilities are sensitive to image qualities. We also evaluated PCA, and it has worse, but constant performance rates because of its general optimization on overall data. Comparing with PCA, Real AdaBoost without any prior distribution performs twice better under good image conditions, but degrades to a performance as good as PCA under poor image conditions.

  17. New feature extraction method for classification of agricultural products from x-ray images

    NASA Astrophysics Data System (ADS)

    Talukder, Ashit; Casasent, David P.; Lee, Ha-Woon; Keagy, Pamela M.; Schatzki, Thomas F.

    1999-01-01

    Classification of real-time x-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a system for automated non- invasive detection of defective product items on a conveyor belt. We discuss the extraction of new features that allow better discrimination between damaged and clean items. This feature extraction and classification stage is the new aspect of this paper; our new maximum representation and discrimination between damaged and clean items. This feature extraction and classification stage is the new aspect of this paper; our new maximum representation and discriminating feature (MRDF) extraction method computes nonlinear features that are used as inputs to a new modified k nearest neighbor classifier. In this work the MRDF is applied to standard features. The MRDF is robust to various probability distributions of the input class and is shown to provide good classification and new ROC data.

  18. Nanoparticles for Cardiovascular Imaging and Therapeutic Delivery, Part 1: Compositions and Features

    PubMed Central

    Stendahl, John C.; Sinusas, Albert J.

    2016-01-01

    Imaging agents made from nanoparticles are functionally versatile and have unique properties that may translate to clinical utility in several key cardiovascular imaging niches. Nanoparticles exhibit size-based circulation, biodistribution, and elimination properties different from those of small molecules and microparticles. In addition, nanoparticles provide versatile platforms that can be engineered to create both multimodal and multifunctional imaging agents with tunable properties. With these features, nanoparticulate imaging agents can facilitate fusion of high-sensitivity and high-resolution imaging modalities and selectively bind tissues for targeted molecular imaging and therapeutic delivery. Despite their intriguing attributes, nanoparticulate imaging agents have thus far achieved only limited clinical use. The reasons for this restricted advancement include an evolving scope of applications, the simplicity and effectiveness of existing small-molecule agents, pharmacokinetic limitations, safety concerns, and a complex regulatory environment. This review describes general features of nanoparticulate imaging agents and therapeutics and discusses challenges associated with clinical translation. A second, related review to appear in a subsequent issue of JNM highlights nuclear-based nanoparticulate probes in preclinical cardiovascular imaging. PMID:26272808

  19. A Partial Intensity Invariant Feature Descriptor for Multimodal Retinal Image Registration

    PubMed Central

    Chen, Jian; Tian, Jie; Lee, Noah; Zheng, Jian; Smith, R. Theodore; Laine, Andrew F.

    2011-01-01

    Detection of vascular bifurcations is a challenging task in multimodal retinal image registration. Existing algorithms based on bifurcations usually fail in correctly aligning poor quality retinal image pairs. To solve this problem, we propose a novel highly distinctive local feature descriptor named partial intensity invariant feature descriptor (PIIFD) and describe a robust automatic retinal image registration framework named Harris-PIIFD. PIIFD is invariant to image rotation, partially invariant to image intensity, affine transformation, and viewpoint/perspective change. Our Harris-PIIFD framework consists of four steps. First, corner points are used as control point candidates instead of bifurcations since corner points are sufficient and uniformly distributed across the image domain. Second, PIIFDs are extracted for all corner points, and a bilateral matching technique is applied to identify corresponding PIIFDs matches between image pairs. Third, incorrect matches are removed and inaccurate matches are refined. Finally, an adaptive transformation is used to register the image pairs. PIIFD is so distinctive that it can be correctly identified even in nonvascular areas. When tested on 168 pairs of multimodal retinal images, the Harris-PIIFD far outperforms existing algorithms in terms of robustness, accuracy, and computational efficiency. PMID:20176538

  20. A partial intensity invariant feature descriptor for multimodal retinal image registration.

    PubMed

    Chen, Jian; Tian, Jie; Lee, Noah; Zheng, Jian; Smith, R Theodore; Laine, Andrew F

    2010-07-01

    Detection of vascular bifurcations is a challenging task in multimodal retinal image registration. Existing algorithms based on bifurcations usually fail in correctly aligning poor quality retinal image pairs. To solve this problem, we propose a novel highly distinctive local feature descriptor named partial intensity invariant feature descriptor (PIIFD) and describe a robust automatic retinal image registration framework named Harris-PIIFD. PIIFD is invariant to image rotation, partially invariant to image intensity, affine transformation, and viewpoint/perspective change. Our Harris-PIIFD framework consists of four steps. First, corner points are used as control point candidates instead of bifurcations since corner points are sufficient and uniformly distributed across the image domain. Second, PIIFDs are extracted for all corner points, and a bilateral matching technique is applied to identify corresponding PIIFDs matches between image pairs. Third, incorrect matches are removed and inaccurate matches are refined. Finally, an adaptive transformation is used to register the image pairs. PIIFD is so distinctive that it can be correctly identified even in nonvascular areas. When tested on 168 pairs of multimodal retinal images, the Harris-PIIFD far outperforms existing algorithms in terms of robustness, accuracy, and computational efficiency.

  1. Cerebral Glioma Grading Using Bayesian Network with Features Extracted from Multiple Modalities of Magnetic Resonance Imaging

    PubMed Central

    Wang, Huiting; Liu, Renyuan; Zhang, Xin; Li, Ming; Yang, Yongbo; Yan, Jing; Niu, Fengnan; Tian, Chuanshuai; Wang, Kun; Yu, Haiping; Chen, Weibo; Wan, Suiren; Sun, Yu; Zhang, Bing

    2016-01-01

    Many modalities of magnetic resonance imaging (MRI) have been confirmed to be of great diagnostic value in glioma grading. Contrast enhanced T1-weighted imaging allows the recognition of blood-brain barrier breakdown. Perfusion weighted imaging and MR spectroscopic imaging enable the quantitative measurement of perfusion parameters and metabolic alterations respectively. These modalities can potentially improve the grading process in glioma if combined properly. In this study, Bayesian Network, which is a powerful and flexible method for probabilistic analysis under uncertainty, is used to combine features extracted from contrast enhanced T1-weighted imaging, perfusion weighted imaging and MR spectroscopic imaging. The networks were constructed using K2 algorithm along with manual determination and distribution parameters learned using maximum likelihood estimation. The grading performance was evaluated in a leave-one-out analysis, achieving an overall grading accuracy of 92.86% and an area under the curve of 0.9577 in the receiver operating characteristic analysis given all available features observed in the total 56 patients. Results and discussions show that Bayesian Network is promising in combining features from multiple modalities of MRI for improved grading performance. PMID:27077923

  2. Cerebral Glioma Grading Using Bayesian Network with Features Extracted from Multiple Modalities of Magnetic Resonance Imaging.

    PubMed

    Hu, Jisu; Wu, Wenbo; Zhu, Bin; Wang, Huiting; Liu, Renyuan; Zhang, Xin; Li, Ming; Yang, Yongbo; Yan, Jing; Niu, Fengnan; Tian, Chuanshuai; Wang, Kun; Yu, Haiping; Chen, Weibo; Wan, Suiren; Sun, Yu; Zhang, Bing

    2016-01-01

    Many modalities of magnetic resonance imaging (MRI) have been confirmed to be of great diagnostic value in glioma grading. Contrast enhanced T1-weighted imaging allows the recognition of blood-brain barrier breakdown. Perfusion weighted imaging and MR spectroscopic imaging enable the quantitative measurement of perfusion parameters and metabolic alterations respectively. These modalities can potentially improve the grading process in glioma if combined properly. In this study, Bayesian Network, which is a powerful and flexible method for probabilistic analysis under uncertainty, is used to combine features extracted from contrast enhanced T1-weighted imaging, perfusion weighted imaging and MR spectroscopic imaging. The networks were constructed using K2 algorithm along with manual determination and distribution parameters learned using maximum likelihood estimation. The grading performance was evaluated in a leave-one-out analysis, achieving an overall grading accuracy of 92.86% and an area under the curve of 0.9577 in the receiver operating characteristic analysis given all available features observed in the total 56 patients. Results and discussions show that Bayesian Network is promising in combining features from multiple modalities of MRI for improved grading performance.

  3. SU-E-J-261: The Importance of Appropriate Image Preprocessing to Augment the Information of Radiomics Image Features

    SciTech Connect

    Zhang, L; Fried, D; Fave, X; Mackin, D; Yang, J; Court, L

    2015-06-15

    Purpose: To investigate how different image preprocessing techniques, their parameters, and the different boundary handling techniques can augment the information of features and improve feature’s differentiating capability. Methods: Twenty-seven NSCLC patients with a solid tumor volume and no visually obvious necrotic regions in the simulation CT images were identified. Fourteen of these patients had a necrotic region visible in their pre-treatment PET images (necrosis group), and thirteen had no visible necrotic region in the pre-treatment PET images (non-necrosis group). We investigated how image preprocessing can impact the ability of radiomics image features extracted from the CT to differentiate between two groups. It is expected the histogram in the necrosis group is more negatively skewed, and the uniformity from the necrosis group is less. Therefore, we analyzed two first order features, skewness and uniformity, on the image inside the GTV in the intensity range [−20HU, 180HU] under the combination of several image preprocessing techniques: (1) applying the isotropic Gaussian or anisotropic diffusion smoothing filter with a range of parameter(Gaussian smoothing: size=11, sigma=0:0.1:2.3; anisotropic smoothing: iteration=4, kappa=0:10:110); (2) applying the boundaryadapted Laplacian filter; and (3) applying the adaptive upper threshold for the intensity range. A 2-tailed T-test was used to evaluate the differentiating capability of CT features on pre-treatment PT necrosis. Result: Without any preprocessing, no differences in either skewness or uniformity were observed between two groups. After applying appropriate Gaussian filters (sigma>=1.3) or anisotropic filters(kappa >=60) with the adaptive upper threshold, skewness was significantly more negative in the necrosis group(p<0.05). By applying the boundary-adapted Laplacian filtering after the appropriate Gaussian filters (0.5 <=sigma<=1.1) or anisotropic filters(20<=kappa <=50), the uniformity was

  4. Automatic segmentation of MR images using self-organizing feature mapping and neural networks

    NASA Astrophysics Data System (ADS)

    Alirezaie, Javad; Jernigan, M. Ed; Nahmias, Claude

    1997-04-01

    In this paper we present an unsupervised clustering technique for multispectral segmentation of magnetic resonance (MR) images of the human brain. Our scheme utilizes the self-organizing feature map (SOFM) artificial neural network (ANN) for feature mapping and generates a set of codebook vectors for each tissue class. Features are selected from three image spectra: T1, T2 and proton density (PD) weighted images. An algorithm has been developed for isolating the cerebrum from the head scan prior to the segmentation. To classify the map, we extend the network by adding an associative layer. Three tissue types of the brain: white matter, gray matter and cerebral spinal fluid (CSF) are segmented accurately. Any unclassified tissues were remained as unknown tissue class.

  5. Nonrigid registration of remote sensing images via sparse and dense feature matching.

    PubMed

    Chen, Jun; Luo, Linbo; Liu, Chengyin; Yu, Jin-Gang; Ma, Jiayi

    2016-07-01

    In this paper, we propose a novel formulation for building pixelwise alignments between remote sensing images under nonrigid transformation based on matching both sparsely and densely sampled features. Our formulation contains two coupling variables: the nonrigid geometric transformation and the discrete dense flow field. To match sparse features, we fit a geometric transformation specified in a reproducing kernel Hilbert space and impose a locally linear constraint to regularize the transformation. To match dense features, we compute a dense flow field by using a formulation analogous to scale invariant feature transform (SIFT) flow which allows nonrigid matching across different scene appearances. An additional term is introduced to ensure the coherence between the two variables, and we alternatively solve for one variable under the assumption that the other is known. Extensive experiments on both synthetic and real remote sensing images demonstrate that our approach greatly outperforms state-of-the-art methods, particularly when the data contain severe degradations. PMID:27409688

  6. Nonrigid registration of remote sensing images via sparse and dense feature matching.

    PubMed

    Chen, Jun; Luo, Linbo; Liu, Chengyin; Yu, Jin-Gang; Ma, Jiayi

    2016-07-01

    In this paper, we propose a novel formulation for building pixelwise alignments between remote sensing images under nonrigid transformation based on matching both sparsely and densely sampled features. Our formulation contains two coupling variables: the nonrigid geometric transformation and the discrete dense flow field. To match sparse features, we fit a geometric transformation specified in a reproducing kernel Hilbert space and impose a locally linear constraint to regularize the transformation. To match dense features, we compute a dense flow field by using a formulation analogous to scale invariant feature transform (SIFT) flow which allows nonrigid matching across different scene appearances. An additional term is introduced to ensure the coherence between the two variables, and we alternatively solve for one variable under the assumption that the other is known. Extensive experiments on both synthetic and real remote sensing images demonstrate that our approach greatly outperforms state-of-the-art methods, particularly when the data contain severe degradations.

  7. Classification of pulmonary nodules in lung CT images using shape and texture features

    NASA Astrophysics Data System (ADS)

    Dhara, Ashis Kumar; Mukhopadhyay, Sudipta; Dutta, Anirvan; Garg, Mandeep; Khandelwal, Niranjan; Kumar, Prafulla

    2016-03-01

    Differentiation of malignant and benign pulmonary nodules is important for prognosis of lung cancer. In this paper, benign and malignant nodules are classified using support vector machine. Several shape-based and texture-based features are used to represent the pulmonary nodules in the feature space. A semi-automated technique is used for nodule segmentation. Relevant features are selected for efficient representation of nodules in the feature space. The proposed scheme and the competing technique are evaluated on a data set of 542 nodules of Lung Image Database Consortium and Image Database Resource Initiative. The nodules with composite rank of malignancy "1","2" are considered as benign and "4","5" are considered as malignant. Area under the receiver operating characteristics curve is 0:9465 for the proposed method. The proposed method outperforms the competing technique.

  8. Selection of the best features for leukocytes classification in blood smear microscopic images

    NASA Astrophysics Data System (ADS)

    Sarrafzadeh, Omid; Rabbani, Hossein; Talebi, Ardeshir; Banaem, Hossein Usefi

    2014-03-01

    Automatic differential counting of leukocytes provides invaluable information to pathologist for diagnosis and treatment of many diseases. The main objective of this paper is to detect leukocytes from a blood smear microscopic image and classify them into their types: Neutrophil, Eosinophil, Basophil, Lymphocyte and Monocyte using features that pathologists consider to differentiate leukocytes. Features contain color, geometric and texture features. Colors of nucleus and cytoplasm vary among the leukocytes. Lymphocytes have single, large, round or oval and Monocytes have singular convoluted shape nucleus. Nucleus of Eosinophils is divided into 2 segments and nucleus of Neutrophils into 2 to 5 segments. Lymphocytes often have no granules, Monocytes have tiny granules, Neutrophils have fine granules and Eosinophils have large granules in cytoplasm. Six color features is extracted from both nucleus and cytoplasm, 6 geometric features only from nucleus and 6 statistical features and 7 moment invariants features only from cytoplasm of leukocytes. These features are fed to support vector machine (SVM) classifiers with one to one architecture. The results obtained by applying the proposed method on blood smear microscopic image of 10 patients including 149 white blood cells (WBCs) indicate that correct rate for all classifiers are above 93% which is in a higher level in comparison with previous literatures.

  9. Utilizing spatial and spectral features of photoacoustic imaging for ovarian cancer detection and diagnosis

    NASA Astrophysics Data System (ADS)

    Li, Hai; Kumavor, Patrick; Salman Alqasemi, Umar; Zhu, Quing

    2015-01-01

    A composite set of ovarian tissue features extracted from photoacoustic spectral data, beam envelope, and co-registered ultrasound and photoacoustic images are used to characterize malignant and normal ovaries using logistic and support vector machine (SVM) classifiers. Normalized power spectra were calculated from the Fourier transform of the photoacoustic beamformed data, from which the spectral slopes and 0-MHz intercepts were extracted. Five features were extracted from the beam envelope and another 10 features were extracted from the photoacoustic images. These 17 features were ranked by their p-values from t-tests on which a filter type of feature selection method was used to determine the optimal feature number for final classification. A total of 169 samples from 19 ex vivo ovaries were randomly distributed into training and testing groups. Both classifiers achieved a minimum value of the mean misclassification error when the seven features with lowest p-values were selected. Using these seven features, the logistic and SVM classifiers obtained sensitivities of 96.39±3.35% and 97.82±2.26%, and specificities of 98.92±1.39% and 100%, respectively, for the training group. For the testing group, logistic and SVM classifiers achieved sensitivities of 92.71±3.55% and 92.64±3.27%, and specificities of 87.52±8.78% and 98.49±2.05%, respectively.

  10. Texture based feature extraction methods for content based medical image retrieval systems.

    PubMed

    Ergen, Burhan; Baykara, Muhammet

    2014-01-01

    The developments of content based image retrieval (CBIR) systems used for image archiving are continued and one of the important research topics. Although some studies have been presented general image achieving, proposed CBIR systems for archiving of medical images are not very efficient. In presented study, it is examined the retrieval efficiency rate of spatial methods used for feature extraction for medical image retrieval systems. The investigated algorithms in this study depend on gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), and Gabor wavelet accepted as spatial methods. In the experiments, the database is built including hundreds of medical images such as brain, lung, sinus, and bone. The results obtained in this study shows that queries based on statistics obtained from GLCM are satisfied. However, it is observed that Gabor Wavelet has been the most effective and accurate method. PMID:25227014

  11. Texture based feature extraction methods for content based medical image retrieval systems.

    PubMed

    Ergen, Burhan; Baykara, Muhammet

    2014-01-01

    The developments of content based image retrieval (CBIR) systems used for image archiving are continued and one of the important research topics. Although some studies have been presented general image achieving, proposed CBIR systems for archiving of medical images are not very efficient. In presented study, it is examined the retrieval efficiency rate of spatial methods used for feature extraction for medical image retrieval systems. The investigated algorithms in this study depend on gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), and Gabor wavelet accepted as spatial methods. In the experiments, the database is built including hundreds of medical images such as brain, lung, sinus, and bone. The results obtained in this study shows that queries based on statistics obtained from GLCM are satisfied. However, it is observed that Gabor Wavelet has been the most effective and accurate method.

  12. Computer-aided classification of breast microcalcification clusters: merging of features from image processing and radiologists

    NASA Astrophysics Data System (ADS)

    Lo, Joseph Y.; Gavrielides, Marios A.; Markey, Mia K.; Jesneck, Jonathan L.

    2003-05-01

    We developed an ensemble classifier for the task of computer-aided diagnosis of breast microcalcification clusters,which are very challenging to characterize for radiologists and computer models alike. The purpose of this study is to help radiologists identify whether suspicious calcification clusters are benign vs. malignant, such that they may potentially recommend fewer unnecessary biopsies for actually benign lesions. The data consists of mammographic features extracted by automated image processing algorithms as well as manually interpreted by radiologists according to a standardized lexicon. We used 292 cases from a publicly available mammography database. From each cases, we extracted 22 image processing features pertaining to lesion morphology, 5 radiologist features also pertaining to morphology, and the patient age. Linear discriminant analysis (LDA) models were designed using each of the three data types. Each local model performed poorly; the best was one based upon image processing features which yielded ROC area index AZ of 0.59 +/- 0.03 and partial AZ above 90% sensitivity of 0.08 +/- 0.03. We then developed ensemble models using different combinations of those data types, and these models all improved performance compared to the local models. The final ensemble model was based upon 5 features selected by stepwise LDA from all 28 available features. This ensemble performed with AZ of 0.69 +/- 0.03 and partial AZ of 0.21 +/- 0.04, which was statistically significantly better than the model based on the image processing features alone (p<0.001 and p=0.01 for full and partial AZ respectively). This demonstrated the value of the radiologist-extracted features as a source of information for this task. It also suggested there is potential for improved performance using this ensemble classifier approach to combine different sources of currently available data.

  13. [Atypical case of bronchial carcinoid].

    PubMed

    Andrzejak, R; Mydłowski, R; Krajewski, E; Orłowski, T; Bochnia, M

    1997-01-01

    This article illustrates problems in diagnosis and treatment of an atypical form of bronchial carcinoid. We described the case of a 49-year old man, exposed to granite dust and noise for 25 years who had suffered from frequent bronchitis inflammations and pneumonias for 5 years prior to the diagnosis. He was admitted to our clinic because of supposed occupational nature of hearing deficiency. Although a pneumoconiosis was excluded before the admission, we found clinical and X-ray features of the right lung emphysema with medium restrictive ventilation disturbances. Bronchoscopy was performed because of "bright" right lung and ventilation disturbances and it showed presence of the carcinoid. Unusual in this case were tiny anamnestical findings (mild dyspnea attacks after physical effort or nervousness) plus increasing frequency of reported from the childhood bronchitis and pneumonias and uncharacteristic "bright" right lung in X-ray. Therapeutical difficulties resulted from atypical histological form of the tumor, its diameter, polypous-infiltrative character, and inconvenient localization. In spite of late diagnosis of carcinoid and significant acceleration of respiratory decompensation symptoms after the diagnosis the attempt of surgical therapy was appropriate but unsuccessful. After the operation the patient was suffering long lasting lowering of arterial pressure (what was corrected with catecholamine infusions) probably as a result of serotonin secretion. However it was not established because of technical reasons.

  14. Water Extraction in High Resolution Remote Sensing Image Based on Hierarchical Spectrum and Shape Features

    NASA Astrophysics Data System (ADS)

    Li, Bangyu; Zhang, Hui; Xu, Fanjiang

    2014-03-01

    This paper addresses the problem of water extraction from high resolution remote sensing images (including R, G, B, and NIR channels), which draws considerable attention in recent years. Previous work on water extraction mainly faced two difficulties. 1) It is difficult to obtain accurate position of water boundary because of using low resolution images. 2) Like all other image based object classification problems, the phenomena of "different objects same image" or "different images same object" affects the water extraction. Shadow of elevated objects (e.g. buildings, bridges, towers and trees) scattered in the remote sensing image is a typical noise objects for water extraction. In many cases, it is difficult to discriminate between water and shadow in a remote sensing image, especially in the urban region. We propose a water extraction method with two hierarchies: the statistical feature of spectral characteristic based on image segmentation and the shape feature based on shadow removing. In the first hierarchy, the Statistical Region Merging (SRM) algorithm is adopted for image segmentation. The SRM includes two key steps: one is sorting adjacent regions according to a pre-ascertained sort function, and the other one is merging adjacent regions based on a pre-ascertained merging predicate. The sort step is done one time during the whole processing without considering changes caused by merging which may cause imprecise results. Therefore, we modify the SRM with dynamic sort processing, which conducts sorting step repetitively when there is large adjacent region changes after doing merging. To achieve robust segmentation, we apply the merging region with six features (four remote sensing image bands, Normalized Difference Water Index (NDWI), and Normalized Saturation-value Difference Index (NSVDI)). All these features contribute to segment image into region of object. NDWI and NSVDI are discriminate between water and some shadows. In the second hierarchy, we adopt

  15. Detection and clustering of features in aerial images by neuron network-based algorithm

    NASA Astrophysics Data System (ADS)

    Vozenilek, Vit

    2015-12-01

    The paper presents the algorithm for detection and clustering of feature in aerial photographs based on artificial neural networks. The presented approach is not focused on the detection of specific topographic features, but on the combination of general features analysis and their use for clustering and backward projection of clusters to aerial image. The basis of the algorithm is a calculation of the total error of the network and a change of weights of the network to minimize the error. A classic bipolar sigmoid was used for the activation function of the neurons and the basic method of backpropagation was used for learning. To verify that a set of features is able to represent the image content from the user's perspective, the web application was compiled (ASP.NET on the Microsoft .NET platform). The main achievements include the knowledge that man-made objects in aerial images can be successfully identified by detection of shapes and anomalies. It was also found that the appropriate combination of comprehensive features that describe the colors and selected shapes of individual areas can be useful for image analysis.

  16. Color Image Segmentation Based on Statistics of Location and Feature Similarity

    NASA Astrophysics Data System (ADS)

    Mori, Fumihiko; Yamada, Hiromitsu; Mizuno, Makoto; Sugano, Naotoshi

    The process of “image segmentation and extracting remarkable regions” is an important research subject for the image understanding. However, an algorithm based on the global features is hardly found. The requisite of such an image segmentation algorism is to reduce as much as possible the over segmentation and over unification. We developed an algorithm using the multidimensional convex hull based on the density as the global feature. In the concrete, we propose a new algorithm in which regions are expanded according to the statistics of the region such as the mean value, standard deviation, maximum value and minimum value of pixel location, brightness and color elements and the statistics are updated. We also introduced a new concept of conspicuity degree and applied it to the various 21 images to examine the effectiveness. The remarkable object regions, which were extracted by the presented system, highly coincided with those which were pointed by the sixty four subjects who attended the psychological experiment.

  17. Image automatic-recognition scheme for dice game using structure features technique

    NASA Astrophysics Data System (ADS)

    Chen, Wen-Yuan; Chung, Chin-Ho

    2011-08-01

    In dice recognition, there are several situations that may cause dice image identification problems, including the color of background being the same as the dice, the shape of the dice being round, or the dice touching each other. Especially when the camera zooms in or out, the pip sizes become irregular. We analyze the dice structure features to develop an intelligent memorized least-distance search method (IMLDSM) that can achieve accurate dice detection. Compared to other schemes, our method has two major advantages: (i) Unlike other schemes using complicated classification methods to segment the dice, we adopt a novel, simple, and effective method to analyze dice structure features to achieve more accurate recognition results and (ii) the IMLDSM solves the problems of image zooming in and out, which other methods cannot. After over 250 test images that include different shapes, styles, sizes, and colors used in simulations, this scheme is proven to achieve 100% dice image identification.

  18. Case report of malignant pulmonary parenchymal glomus tumor: imaging features and review of the literature.

    PubMed

    Cunningham, Jane D; Plodkowski, Andrew J; Giri, Dilip D; Hwang, Sinchun

    2016-01-01

    Glomus tumor is rare tumor which arises from glomus body and is most frequently found in the soft tissue of the extremities. The lung is a rare ectopic site, and a malignant glomus tumor arising from pulmonary parenchyma is particularly uncommon. To deepen our understanding on their imaging features, we report a case of malignant glomus tumor of pulmonary parenchyma confirmed with surgical histopathology and immunochemistry and review the medical literature on pulmonary parenchymal glomus tumors with emphasis on their imaging features. PMID:26498485

  19. Feature selection and classification of multiparametric medical images using bagging and SVM

    NASA Astrophysics Data System (ADS)

    Fan, Yong; Resnick, Susan M.; Davatzikos, Christos

    2008-03-01

    This paper presents a framework for brain classification based on multi-parametric medical images. This method takes advantage of multi-parametric imaging to provide a set of discriminative features for classifier construction by using a regional feature extraction method which takes into account joint correlations among different image parameters; in the experiments herein, MRI and PET images of the brain are used. Support vector machine classifiers are then trained based on the most discriminative features selected from the feature set. To facilitate robust classification and optimal selection of parameters involved in classification, in view of the well-known "curse of dimensionality", base classifiers are constructed in a bagging (bootstrap aggregating) framework for building an ensemble classifier and the classification parameters of these base classifiers are optimized by means of maximizing the area under the ROC (receiver operating characteristic) curve estimated from their prediction performance on left-out samples of bootstrap sampling. This classification system is tested on a sex classification problem, where it yields over 90% classification rates for unseen subjects. The proposed classification method is also compared with other commonly used classification algorithms, with favorable results. These results illustrate that the methods built upon information jointly extracted from multi-parametric images have the potential to perform individual classification with high sensitivity and specificity.

  20. Automated Feature Extraction in Brain Tumor by Magnetic Resonance Imaging Using Gaussian Mixture Models

    PubMed Central

    Chaddad, Ahmad

    2015-01-01

    This paper presents a novel method for Glioblastoma (GBM) feature extraction based on Gaussian mixture model (GMM) features using MRI. We addressed the task of the new features to identify GBM using T1 and T2 weighted images (T1-WI, T2-WI) and Fluid-Attenuated Inversion Recovery (FLAIR) MR images. A pathologic area was detected using multithresholding segmentation with morphological operations of MR images. Multiclassifier techniques were considered to evaluate the performance of the feature based scheme in terms of its capability to discriminate GBM and normal tissue. GMM features demonstrated the best performance by the comparative study using principal component analysis (PCA) and wavelet based features. For the T1-WI, the accuracy performance was 97.05% (AUC = 92.73%) with 0.00% missed detection and 2.95% false alarm. In the T2-WI, the same accuracy (97.05%, AUC = 91.70%) value was achieved with 2.95% missed detection and 0.00% false alarm. In FLAIR mode the accuracy decreased to 94.11% (AUC = 95.85%) with 0.00% missed detection and 5.89% false alarm. These experimental results are promising to enhance the characteristics of heterogeneity and hence early treatment of GBM. PMID:26136774

  1. Binarization of color document images via luminance and saturation color features.

    PubMed

    Tsai, Chun-Ming; Lee, Hsi-Jian

    2002-01-01

    This paper presents a novel binarization algorithm for color document images. Conventional thresholding methods do not produce satisfactory binarization results for documents with close or mixed foreground colors and background colors. Initially, statistical image features are extracted from the luminance distribution. Then, a decision-tree based binarization method is proposed, which selects various color features to binarize color document images. First, if the document image colors are concentrated within a limited range, saturation is employed. Second, if the image foreground colors are significant, luminance is adopted. Third, if the image background colors are concentrated within a limited range, luminance is also applied. Fourth, if the total number of pixels with low luminance (less than 60) is limited, saturation is applied; else both luminance and saturation are employed. Our experiments include 519 color images, most of which are uniform invoice and name-card document images. The proposed binarization method generates better results than other available methods in shape and connected-component measurements. Also, the binarization method obtains higher recognition accuracy in a commercial OCR system than other comparable methods. PMID:18244645

  2. Image feature analysis for classification of microcalcifications in digital mammography: neural networks and genetic algorithms

    NASA Astrophysics Data System (ADS)

    Wu, Chris Y.; Tsujii, Osamu; Freedman, Matthew T.; Mun, Seong K.

    1997-04-01

    We have developed an image feature-based algorithm to classify microcalcifications associated with benign and malignant processes in digital mammograms for the diagnosis of breast cancer. The feature-based algorithm is an alternative approach to image based method for classification of microcalcifications in digital mammograms. Microcalcifications can be characterized by a number of quantitative variables describing the underling key features of a suspicious region such as the size, shape, and number of microcalcifications in a cluster. These features are calculated by an automated extraction scheme for each of the selected regions. The features are then used as input to a backpropagation neural network to make a decision regarding the probability of malignancy of a selected region. The initial selection of image features set is a rough estimation that may include redundant and non-discriminant features. A genetic algorithm is employed to select an optimal image feature set from the initial feature set and select an optimized structure of the neural network for the optimal input features. The performance of neural network is compared with that of radiologists in classifying the clusters of microcalcifications. Two set of mammogram cases are used in this study. The first set is from the digital mammography database from the Mammographic Image Analysis Society (MIAS). The second set is from cases collected at Georgetown University Medical Center (GUMC). The diagnostic truth of the cases have been verified by biopsy. The performance of the neural network system is evaluated by ROC analysis. The system of neural network and genetic algorithms improves performance of our previous TRBF neural network. The neural network system was able to classify benign and malignant microcalcifications at a level favorably compared to experienced radiologists. The use of the neural network system can be used to help radiologists reducing the number biopsies in clinical applications

  3. Atypical Apocrine Adenosis: Diagnostic Challenges and Pitfalls.

    PubMed

    Asirvatham, Jaya Ruth; Falcone, Maria Monica Garcia; Kleer, Celina G

    2016-10-01

    Apocrine change in the breast is an extremely common finding. In most cases, the benign or malignant nature of the lesion is easily recognized. Apocrine adenosis is used to describe sclerosing adenosis with apocrine change. The term apocrine atypia is used when there is significant cytologic atypia in apocrine cells, characterized by a 3-fold nuclear enlargement, prominent/multiple nucleoli, and hyperchromasia. Atypical apocrine adenosis is diagnosed when apocrine adenosis and apocrine atypia are superimposed. However, there are no definite criteria to distinguish atypical apocrine adenosis from apocrine ductal carcinoma in situ. Immunohistochemical markers can be confounding and may lead to erroneous diagnoses. Atypical apocrine features in sclerosing lesions may be misinterpreted as invasive carcinoma if the underlying lesion is not recognized. In the absence of definite features of malignancy, the diagnosis of apocrine ductal carcinoma in situ may be extremely difficult. In the present article, we review atypical apocrine adenosis focusing on diagnostic challenges and their implications on clinical management. PMID:27684975

  4. Segmenting texts from outdoor images taken by mobile phones using color features

    NASA Astrophysics Data System (ADS)

    Liu, Zongyi; Zhou, Hanning

    2011-01-01

    Recognizing texts from images taken by mobile phones with low resolution has wide applications. It has been shown that a good image binarization can substantially improve the performances of OCR engines. In this paper, we present a framework to segment texts from outdoor images taken by mobile phones using color features. The framework consists of three steps: (i) the initial process including image enhancement, binarization and noise filtering, where we binarize the input images in each RGB channel, and apply component level noise filtering; (ii) grouping components into blocks using color features, where we compute the component similarities by dynamically adjusting the weights of RGB channels, and merge groups hierachically, and (iii) blocks selection, where we use the run-length features and choose the Support Vector Machine (SVM) as the classifier. We tested the algorithm using 13 outdoor images taken by an old-style LG-64693 mobile phone with 640x480 resolution. We compared the segmentation results with Tsar's algorithm, a state-of-the-art camera text detection algorithm, and show that our algorithm is more robust, particularly in terms of the false alarm rates. In addition, we also evaluated the impacts of our algorithm on the Abbyy's FineReader, one of the most popular commercial OCR engines in the market.

  5. Probability-based diagnostic imaging using hybrid features extracted from ultrasonic Lamb wave signals

    NASA Astrophysics Data System (ADS)

    Zhou, Chao; Su, Zhongqing; Cheng, Li

    2011-12-01

    The imaging technique based on guided waves has been a research focus in the field of damage detection over the years, aimed at intuitively highlighting structural damage in two- or three-dimensional images. The accuracy and efficiency of this technique substantially rely on the means of defining the field values at image pixels. In this study, a novel probability-based diagnostic imaging (PDI) approach was developed. Hybrid signal features (including temporal information, intensity of signal energy and signal correlation) were extracted from ultrasonic Lamb wave signals and integrated to retrofit the traditional way of defining field values. To acquire hybrid signal features, an active sensor network in line with pulse-echo and pitch-catch configurations was designed, supplemented with a novel concept of 'virtual sensing'. A hybrid image fusion scheme was developed to enhance the tolerance of the approach to measurement noise/uncertainties and erroneous perceptions from individual sensors. As applications, the approach was employed to identify representative damage scenarios including L-shape through-thickness crack (orientation-specific damage), polygonal damage (multi-edge damage) and multi-damage in structural plates. Results have corroborated that the developed PDI approach based on the use of hybrid signal features is capable of visualizing structural damage quantitatively, regardless of damage shape and number, by highlighting its individual edges in an easily interpretable binary image.

  6. Spectrum and Image Texture Features Analysis for Early Blight Disease Detection on Eggplant Leaves.

    PubMed

    Xie, Chuanqi; He, Yong

    2016-01-01

    This study investigated both spectrum and texture features for detecting early blight disease on eggplant leaves. Hyperspectral images for healthy and diseased samples were acquired covering the wavelengths from 380 to 1023 nm. Four gray images were identified according to the effective wavelengths (408, 535, 624 and 703 nm). Hyperspectral images were then converted into RGB, HSV and HLS images. Finally, eight texture features (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation) based on gray level co-occurrence matrix (GLCM) were extracted from gray images, RGB, HSV and HLS images, respectively. The dependent variables for healthy and diseased samples were set as 0 and 1. K-Nearest Neighbor (KNN) and AdaBoost classification models were established for detecting healthy and infected samples. All models obtained good results with the classification rates (CRs) over 88.46% in the testing sets. The results demonstrated that spectrum and texture features were effective for early blight disease detection on eggplant leaves. PMID:27187387

  7. Spectrum and Image Texture Features Analysis for Early Blight Disease Detection on Eggplant Leaves

    PubMed Central

    Xie, Chuanqi; He, Yong

    2016-01-01

    This study investigated both spectrum and texture features for detecting early blight disease on eggplant leaves. Hyperspectral images for healthy and diseased samples were acquired covering the wavelengths from 380 to 1023 nm. Four gray images were identified according to the effective wavelengths (408, 535, 624 and 703 nm). Hyperspectral images were then converted into RGB, HSV and HLS images. Finally, eight texture features (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation) based on gray level co-occurrence matrix (GLCM) were extracted from gray images, RGB, HSV and HLS images, respectively. The dependent variables for healthy and diseased samples were set as 0 and 1. K-Nearest Neighbor (KNN) and AdaBoost classification models were established for detecting healthy and infected samples. All models obtained good results with the classification rates (CRs) over 88.46% in the testing sets. The results demonstrated that spectrum and texture features were effective for early blight disease detection on eggplant leaves. PMID:27187387

  8. An image processing approach to analyze morphological features of microscopic images of muscle fibers

    PubMed Central

    Comin, Cesar Henrique; Xu, Xiaoyin; Wang, Yaming; da Fontoura Costa, Luciano; Yang, Zhong

    2016-01-01

    We present an image processing approach to automatically analyze duo-channel microscopic images of muscular fiber nuclei and cytoplasm. Nuclei and cytoplasm play a critical role in determining the health and functioning of muscular fibers as changes of nuclei and cytoplasm manifest in many diseases such as muscular dystrophy and hypertrophy. Quantitative evaluation of muscle fiber nuclei and cytoplasm thus is of great importance to researchers in musculoskeletal studies. The proposed computational approach consists of steps of image processing to segment and delineate cytoplasm and identify nuclei in two-channel images. Morphological operations like skeletonization is applied to extract the length of cytoplasm for quantification. We tested the approach on real images and found that it can achieve high accuracy, objectivity, and robustness. PMID:25124286

  9. An image processing approach to analyze morphological features of microscopic images of muscle fibers.

    PubMed

    Comin, Cesar Henrique; Xu, Xiaoyin; Wang, Yaming; Costa, Luciano da Fontoura; Yang, Zhong

    2014-12-01

    We present an image processing approach to automatically analyze duo-channel microscopic images of muscular fiber nuclei and cytoplasm. Nuclei and cytoplasm play a critical role in determining the health and functioning of muscular fibers as changes of nuclei and cytoplasm manifest in many diseases such as muscular dystrophy and hypertrophy. Quantitative evaluation of muscle fiber nuclei and cytoplasm thus is of great importance to researchers in musculoskeletal studies. The proposed computational approach consists of steps of image processing to segment and delineate cytoplasm and identify nuclei in two-channel images. Morphological operations like skeletonization is applied to extract the length of cytoplasm for quantification. We tested the approach on real images and found that it can achieve high accuracy, objectivity, and robustness.

  10. Geometrically robust image watermarking using scale-invariant feature transform and Zernike moments

    NASA Astrophysics Data System (ADS)

    Li, Leida; Guo, Baolong; Shao, Kai

    2007-06-01

    In order to resist geometric attacks, a robust image watermarking algorithm is proposed using scale-invariant feature transform (SIFT) and Zernike moments. As SIFT features are invariant to rotation and scaling, we employ SIFT to extract feature points. Then circular patches are generated using the most robust points. An invariant watermark is generated from each circular patch based on Zernike moments. The watermark is embedded into multiple patches for resisting locally cropping attacks. Experimental results show that the proposed scheme is robust to both geometric attacks and signal processing attacks.

  11. A graph lattice approach to maintaining and learning dense collections of subgraphs as image features.

    PubMed

    Saund, Eric

    2013-10-01

    Effective object and scene classification and indexing depend on extraction of informative image features. This paper shows how large families of complex image features in the form of subgraphs can be built out of simpler ones through construction of a graph lattice—a hierarchy of related subgraphs linked in a lattice. Robustness is achieved by matching many overlapping and redundant subgraphs, which allows the use of inexpensive exact graph matching, instead of relying on expensive error-tolerant graph matching to a minimal set of ideal model graphs. Efficiency in exact matching is gained by exploitation of the graph lattice data structure. Additionally, the graph lattice enables methods for adaptively growing a feature space of subgraphs tailored to observed data. We develop the approach in the domain of rectilinear line art, specifically for the practical problem of document forms recognition. We are especially interested in methods that require only one or very few labeled training examples per category. We demonstrate two approaches to using the subgraph features for this purpose. Using a bag-of-words feature vector we achieve essentially single-instance learning on a benchmark forms database, following an unsupervised clustering stage. Further performance gains are achieved on a more difficult dataset using a feature voting method and feature selection procedure.

  12. Method for optical coherence tomography image classification using local features and earth mover's distance

    NASA Astrophysics Data System (ADS)

    Sun, Yankui; Lei, Ming

    2009-09-01

    Optical coherence tomography (OCT) is a recent imaging method that allows high-resolution, cross-sectional imaging through tissues and materials. Over the past 18 years, OCT has been successfully used in disease diagnosis, biomedical research, material evaluation, and many other domains. As OCT is a recent imaging method, until now surgeons have limited experience using it. In addition, the number of images obtained from the imaging device is too large, so we need an automated method to analyze them. We propose a novel method for automated classification of OCT images based on local features and earth mover's distance (EMD). We evaluated our algorithm using an OCT image set which contains two kinds of skin images, normal skin and nevus flammeus. Experimental results demonstrate the effectiveness of our method, which achieved classification accuracy of 0.97 for an EMD+KNN scheme and 0.99 for an EMD+SVM (support vector machine) scheme, much higher than the previous method. Our approach is especially suitable for nonhomogeneous images and could be applied to a wide range of OCT images.

  13. Medical X-ray Image Hierarchical Classification Using a Merging and Splitting Scheme in Feature Space.

    PubMed

    Fesharaki, Nooshin Jafari; Pourghassem, Hossein

    2013-07-01

    Due to the daily mass production and the widespread variation of medical X-ray images, it is necessary to classify these for searching and retrieving proposes, especially for content-based medical image retrieval systems. In this paper, a medical X-ray image hierarchical classification structure based on a novel merging and splitting scheme and using shape and texture features is proposed. In the first level of the proposed structure, to improve the classification performance, similar classes with regard to shape contents are grouped based on merging measures and shape features into the general overlapped classes. In the next levels of this structure, the overlapped classes split in smaller classes based on the classification performance of combination of shape and texture features or texture features only. Ultimately, in the last levels, this procedure is also continued forming all the classes, separately. Moreover, to optimize the feature vector in the proposed structure, we use orthogonal forward selection algorithm according to Mahalanobis class separability measure as a feature selection and reduction algorithm. In other words, according to the complexity and inter-class distance of each class, a sub-space of the feature space is selected in each level and then a supervised merging and splitting scheme is applied to form the hierarchical classification. The proposed structure is evaluated on a database consisting of 2158 medical X-ray images of 18 classes (IMAGECLEF 2005 database) and accuracy rate of 93.6% in the last level of the hierarchical structure for an 18-class classification problem is obtained.

  14. Pontine Infarct Presenting with Atypical Dental Pain: A Case Report.

    PubMed

    Goel, Rajat; Kumar, Sanjeev; Panwar, Ajay; Singh, Abhishek B

    2015-01-01

    Orofacial pain' most commonly occurs due to dental causes like caries, gingivitis or periodontitis. Other common causes of 'orofacial pain' are sinusitis, temporomandibular joint(TMJ) dysfunction, otitis externa, tension headache and migraine. In some patients, the etiology of 'orofacial pain' remains undetected despite optimal evaluation. A few patients in the practice of clinical dentistry presents with dental pain without any identifiable dental etiology. Such patients are classified under the category of 'atypical odontalgia'. 'Atypical odontalgia' is reported to be prevalent in 2.1% of the individuals. 'Atypical orofacial pain' and 'atypical odontalgia' can result from the neurological diseases like multiple sclerosis, trigeminal neuralgia and herpes infection. Trigeminal neuralgia has been frequently documented as a cause of 'atypical orofacial pain' and 'atypical odontalgia'. There are a few isolated case reports of acute pontine stroke resulting in 'atypical orofacial pain' and 'atypical odontalgia'. However, pontine stroke as a cause of atypical odontalgia is limited to only a few cases, hence prevalence is not established. This case is one, where a patient presented with acute onset atypical dental pain with no identifiable dental etiology, further diagnosed as an acute pontine infarct on neuroimaging. A 40 years old male presented with acute onset, diffuse teeth pain on right side. Dental examination was normal. Magnetic resonance imaging(MRI) of the brain had an acute infarct in right pons near the trigeminal root entry zone(REZ). Pontine infarct presenting with dental pain as a manifestation of trigeminal neuropathy, has rarely been reported previously. This stresses on the importance of neuroradiology in evaluation of atypical cases of dental pain. PMID:26464604

  15. Pontine Infarct Presenting with Atypical Dental Pain: A Case Report

    PubMed Central

    Goel, Rajat; Kumar, Sanjeev; Panwar, Ajay; Singh, Abhishek B

    2015-01-01

    Orofacial pain’ most commonly occurs due to dental causes like caries, gingivitis or periodontitis. Other common causes of ‘orofacial pain’ are sinusitis, temporomandibular joint(TMJ) dysfunction, otitis externa, tension headache and migraine. In some patients, the etiology of ‘orofacial pain’ remains undetected despite optimal evaluation. A few patients in the practice of clinical dentistry presents with dental pain without any identifiable dental etiology. Such patients are classified under the category of ‘atypical odontalgia’. ‘Atypical odontalgia’ is reported to be prevalent in 2.1% of the individuals. ‘Atypical orofacial pain’ and ‘atypical odontalgia’ can result from the neurological diseases like multiple sclerosis, trigeminal neuralgia and herpes infection. Trigeminal neuralgia has been frequently documented as a cause of ‘atypical orofacial pain’ and ‘atypical odontalgia’. There are a few isolated case reports of acute pontine stroke resulting in ‘atypical orofacial pain’ and ‘atypical odontalgia’. However, pontine stroke as a cause of atypical odontalgia is limited to only a few cases, hence prevalence is not established. This case is one, where a patient presented with acute onset atypical dental pain with no identifiable dental etiology, further diagnosed as an acute pontine infarct on neuroimaging. A 40 years old male presented with acute onset, diffuse teeth pain on right side. Dental examination was normal. Magnetic resonance imaging(MRI) of the brain had an acute infarct in right pons near the trigeminal root entry zone(REZ). Pontine infarct presenting with dental pain as a manifestation of trigeminal neuropathy, has rarely been reported previously. This stresses on the importance of neuroradiology in evaluation of atypical cases of dental pain. PMID:26464604

  16. Learning a structured graphical model with boosted top-down features for ultrasound image segmentation.

    PubMed

    Hao, Zhihui; Wang, Qiang; Wang, Xiaotao; Kim, Jung Bae; Hwang, Youngkyoo; Cho, Baek Hwan; Guo, Ping; Lee, Won Ki

    2013-01-01

    A key problem for many medical image segmentation tasks is the combination of different-level knowledge. We propose a novel scheme of embedding detected regions into a superpixel based graphical model, by which we achieve a full leverage on various image cues for ultrasound lesion segmentation. Region features are mapped into a higher-dimensional space via a boosted model to become well controlled. Parameters for regions, superpixels and a new affinity term are learned simultaneously within the framework of structured learning. Experiments on a breast ultrasound image data set confirm the effectiveness of the proposed approach as well as our two novel modules.

  17. Aircraft detection from VHR images based on circle-frequency filter and multilevel features.

    PubMed

    Gao, Feng; Xu, Qizhi; Li, Bo

    2013-01-01

    Aircraft automatic detection from very high-resolution (VHR) images plays an important role in a wide variety of applications. This paper proposes a novel detector for aircraft detection from very high-resolution (VHR) remote sensing images. To accurately distinguish aircrafts from background, a circle-frequency filter (CF-filter) is used to extract the candidate locations of aircrafts from a large size image. A multi-level feature model is then employed to represent both local appearance and spatial layout of aircrafts by means of Robust Hue Descriptor and Histogram of Oriented Gradients. The experimental results demonstrate the superior performance of the proposed method.

  18. Reduction of false-positive recalls using a computerized mammographic image feature analysis scheme

    NASA Astrophysics Data System (ADS)

    Tan, Maxine; Pu, Jiantao; Zheng, Bin

    2014-08-01

    The high false-positive recall rate is one of the major dilemmas that significantly reduce the efficacy of screening mammography, which harms a large fraction of women and increases healthcare cost. This study aims to investigate the feasibility of helping reduce false-positive recalls by developing a new computer-aided diagnosis (CAD) scheme based on the analysis of global mammographic texture and density features computed from four-view images. Our database includes full-field digital mammography (FFDM) images acquired from 1052 recalled women (669 positive for cancer and 383 benign). Each case has four images: two craniocaudal (CC) and two mediolateral oblique (MLO) views. Our CAD scheme first computed global texture features related to the mammographic density distribution on the segmented breast regions of four images. Second, the computed features were given to two artificial neural network (ANN) classifiers that were separately trained and tested in a ten-fold cross-validation scheme on CC and MLO view images, respectively. Finally, two ANN classification scores were combined using a new adaptive scoring fusion method that automatically determined the optimal weights to assign to both views. CAD performance was tested using the area under a receiver operating characteristic curve (AUC). The AUC = 0.793  ±  0.026 was obtained for this four-view CAD scheme, which was significantly higher at the 5% significance level than the AUCs achieved when using only CC (p = 0.025) or MLO (p = 0.0004) view images, respectively. This study demonstrates that a quantitative assessment of global mammographic image texture and density features could provide useful and/or supplementary information to classify between malignant and benign cases among the recalled cases, which may eventually help reduce the false-positive recall rate in screening mammography.

  19. Spinal focal lesion detection in multiple myeloma using multimodal image features

    NASA Astrophysics Data System (ADS)

    Fränzle, Andrea; Hillengass, Jens; Bendl, Rolf

    2015-03-01

    Multiple myeloma is a tumor disease in the bone marrow that affects the skeleton systemically, i.e. multiple lesions can occur in different sites in the skeleton. To quantify overall tumor mass for determining degree of disease and for analysis of therapy response, volumetry of all lesions is needed. Since the large amount of lesions in one patient impedes manual segmentation of all lesions, quantification of overall tumor volume is not possible until now. Therefore development of automatic lesion detection and segmentation methods is necessary. Since focal tumors in multiple myeloma show different characteristics in different modalities (changes in bone structure in CT images, hypointensity in T1 weighted MR images and hyperintensity in T2 weighted MR images), multimodal image analysis is necessary for the detection of focal tumors. In this paper a pattern recognition approach is presented that identifies focal lesions in lumbar vertebrae based on features from T1 and T2 weighted MR images. Image voxels within bone are classified using random forests based on plain intensities and intensity value derived features (maximum, minimum, mean, median) in a 5 x 5 neighborhood around a voxel from both T1 and T2 weighted MR images. A test data sample of lesions in 8 lumbar vertebrae from 4 multiple myeloma patients can be classified at an accuracy of 95% (using a leave-one-patient-out test). The approach provides a reasonable delineation of the example lesions. This is an important step towards automatic tumor volume quantification in multiple myeloma.

  20. Feature-based fuzzy connectedness segmentation of ultrasound images with an object completion step

    PubMed Central

    Rueda, Sylvia; Knight, Caroline L.; Papageorghiou, Aris T.; Alison Noble, J.

    2015-01-01

    Medical ultrasound (US) image segmentation and quantification can be challenging due to signal dropouts, missing boundaries, and presence of speckle, which gives images of similar objects quite different appearance. Typically, purely intensity-based methods do not lead to a good segmentation of the structures of interest. Prior work has shown that local phase and feature asymmetry, derived from the monogenic signal, extract structural information from US images. This paper proposes a new US segmentation approach based on the fuzzy connectedness framework. The approach uses local phase and feature asymmetry to define a novel affinity function, which drives the segmentation algorithm, incorporates a shape-based object completion step, and regularises the result by mean curvature flow. To appreciate the accuracy and robustness of the methodology across clinical data of varying appearance and quality, a novel entropy-based quantitative image quality assessment of the different regions of interest is introduced. The new method is applied to 81 US images of the fetal arm acquired at multiple gestational ages, as a means to define a new automated image-based biomarker of fetal nutrition. Quantitative and qualitative evaluation shows that the segmentation method is comparable to manual delineations and robust across image qualities that are typical of clinical practice. PMID:26319973

  1. Singular Value Decomposition Based Features for Automatic Tumor Detection in Wireless Capsule Endoscopy Images.

    PubMed

    Faghih Dinevari, Vahid; Karimian Khosroshahi, Ghader; Zolfy Lighvan, Mina

    2016-01-01

    Wireless capsule endoscopy (WCE) is a new noninvasive instrument which allows direct observation of the gastrointestinal tract to diagnose its relative diseases. Because of the large number of images obtained from the capsule endoscopy per patient, doctors need too much time to investigate all of them. So, it would be worthwhile to design a system for detecting diseases automatically. In this paper, a new method is presented for automatic detection of tumors in the WCE images. This method will utilize the advantages of the discrete wavelet transform (DWT) and singular value decomposition (SVD) algorithms to extract features from different color channels of the WCE images. Therefore, the extracted features are invariant to rotation and can describe multiresolution characteristics of the WCE images. In order to classify the WCE images, the support vector machine (SVM) method is applied to a data set which includes 400 normal and 400 tumor WCE images. The experimental results show proper performance of the proposed algorithm for detection and isolation of the tumor images which, in the best way, shows 94%, 93%, and 93.5% of sensitivity, specificity, and accuracy in the RGB color space, respectively. PMID:27478364

  2. Singular Value Decomposition Based Features for Automatic Tumor Detection in Wireless Capsule Endoscopy Images

    PubMed Central

    Karimian Khosroshahi, Ghader; Zolfy Lighvan, Mina

    2016-01-01

    Wireless capsule endoscopy (WCE) is a new noninvasive instrument which allows direct observation of the gastrointestinal tract to diagnose its relative diseases. Because of the large number of images obtained from the capsule endoscopy per patient, doctors need too much time to investigate all of them. So, it would be worthwhile to design a system for detecting diseases automatically. In this paper, a new method is presented for automatic detection of tumors in the WCE images. This method will utilize the advantages of the discrete wavelet transform (DWT) and singular value decomposition (SVD) algorithms to extract features from different color channels of the WCE images. Therefore, the extracted features are invariant to rotation and can describe multiresolution characteristics of the WCE images. In order to classify the WCE images, the support vector machine (SVM) method is applied to a data set which includes 400 normal and 400 tumor WCE images. The experimental results show proper performance of the proposed algorithm for detection and isolation of the tumor images which, in the best way, shows 94%, 93%, and 93.5% of sensitivity, specificity, and accuracy in the RGB color space, respectively. PMID:27478364

  3. Benign Conditions That Mimic Prostate Carcinoma: MR Imaging Features with Histopathologic Correlation.

    PubMed

    Kitzing, Yu Xuan; Prando, Adilson; Varol, Celi; Karczmar, Gregory S; Maclean, Fiona; Oto, Aytekin

    2016-01-01

    Multiparametric magnetic resonance (MR) imaging combines anatomic and functional imaging techniques for evaluating the prostate and is increasingly being used in diagnosis and management of prostate cancer. A wide spectrum of anatomic and pathologic processes in the prostate may masquerade as prostate cancer, complicating the imaging interpretation. The histopathologic and imaging findings of these potential mimics are reviewed. These entities include the anterior fibromuscular stroma, surgical capsule, central zone, periprostatic vein, periprostatic lymph nodes, benign prostatic hyperplasia (BPH), atrophy, necrosis, calcification, hemorrhage, and prostatitis. An understanding of the prostate zonal anatomy is helpful in distinguishing the anatomic entities from prostate cancer. The anterior fibromuscular stroma, surgical capsule, and central zone are characteristic anatomic features of the prostate with associated low T2 signal intensity due to dense fibromuscular tissue or complex crowded glandular tissue. BPH, atrophy, necrosis, calcification, and hemorrhage all have characteristic features with one or more individual multiparametric MR imaging modalities. Prostatitis constitutes a heterogeneous group of infective and inflammatory conditions including acute and chronic bacterial prostatitis, infective and noninfective granulomatous prostatitis, and malacoplakia. These entities are associated with variable clinical manifestations and are characterized by the histologic hallmark of marked inflammatory cellular infiltration. In some cases, these entities are indistinguishable from prostate cancer at multiparametric MR imaging and may even exhibit extraprostatic extension and lymphadenopathy, mimicking locally advanced prostate cancer. It is important for the radiologists interpreting prostate MR images to be aware of these pitfalls for accurate interpretation. Online supplemental material is available for this article.

  4. Feature extraction and classification for ultrasound images of lumbar spine with support vector machine.

    PubMed

    Yu, Shuang; Tan, Kok Kiong; Sng, Ban Leong; Li, Shengjin; Sia, Alex Tiong Heng

    2014-01-01

    In this paper, we proposed a feature extraction and machine learning method for the classification of ultrasound images obtained from lumbar spine of pregnant patients in the transverse plane. A group of features, including matching values and positions, appearance of black pixels within predefined windows along the midline, are extracted from the ultrasound images using template matching and midline detection. Support vector machine (SVM) with Gaussian kernel is utilized to classify the bone images and interspinous images with optimal separation hyperplane. The SVM is trained with 800 images from 20 pregnant subjects and tested with 640 images from a separate set of 16 pregnant patients. A high success rate (97.25% on training set and 95.00% on test set) is achieved with the proposed method. The trained SVM model is further tested on 36 videos collected from 36 pregnant subjects and successfully identified the proper needle insertion site (interspinous region) on all of the cases. Therefore, the proposed method is able to identify the ultrasound images of lumbar spine in an automatic manner, so as to facilitate the anesthetists' work to identify the needle insertion point precisely and effectively.

  5. Segmentation of Polarimetric SAR Images Usig Wavelet Transformation and Texture Features

    NASA Astrophysics Data System (ADS)

    Rezaeian, A.; Homayouni, S.; Safari, A.

    2015-12-01

    Polarimetric Synthetic Aperture Radar (PolSAR) sensors can collect useful observations from earth's surfaces and phenomena for various remote sensing applications, such as land cover mapping, change and target detection. These data can be acquired without the limitations of weather conditions, sun illumination and dust particles. As result, SAR images, and in particular Polarimetric SAR (PolSAR) are powerful tools for various environmental applications. Unlike the optical images, SAR images suffer from the unavoidable speckle, which causes the segmentation of this data difficult. In this paper, we use the wavelet transformation for segmentation of PolSAR images. Our proposed method is based on the multi-resolution analysis of texture features is based on wavelet transformation. Here, we use the information of gray level value and the information of texture. First, we produce coherency or covariance matrices and then generate span image from them. In the next step of proposed method is texture feature extraction from sub-bands is generated from discrete wavelet transform (DWT). Finally, PolSAR image are segmented using clustering methods as fuzzy c-means (FCM) and k-means clustering. We have applied the proposed methodology to full polarimetric SAR images acquired by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) L-band system, during July, in 2012 over an agricultural area in Winnipeg, Canada.

  6. Development of estimation system of knee extension strength using image features in ultrasound images of rectus femoris

    NASA Astrophysics Data System (ADS)

    Murakami, Hiroki; Watanabe, Tsuneo; Fukuoka, Daisuke; Terabayashi, Nobuo; Hara, Takeshi; Muramatsu, Chisako; Fujita, Hiroshi

    2016-04-01

    The word "Locomotive syndrome" has been proposed to describe the state of requiring care by musculoskeletal disorders and its high-risk condition. Reduction of the knee extension strength is cited as one of the risk factors, and the accurate measurement of the strength is needed for the evaluation. The measurement of knee extension strength using a dynamometer is one of the most direct and quantitative methods. This study aims to develop a system for measuring the knee extension strength using the ultrasound images of the rectus femoris muscles obtained with non-invasive ultrasonic diagnostic equipment. First, we extract the muscle area from the ultrasound images and determine the image features, such as the thickness of the muscle. We combine these features and physical features, such as the patient's height, and build a regression model of the knee extension strength from training data. We have developed a system for estimating the knee extension strength by applying the regression model to the features obtained from test data. Using the test data of 168 cases, correlation coefficient value between the measured values and estimated values was 0.82. This result suggests that this system can estimate knee extension strength with high accuracy.

  7. DBSCAN-based ROI extracted from SAR images and the discrimination of multi-feature ROI

    NASA Astrophysics Data System (ADS)

    He, Xin Yi; Zhao, Bo; Tan, Shu Run; Zhou, Xiao Yang; Jiang, Zhong Jin; Cui, Tie Jun

    2009-10-01

    The purpose of the paper is to extract the region of interest (ROI) from the coarse detected synthetic aperture radar (SAR) images and discriminate if the ROI contains a target or not, so as to eliminate the false alarm, and prepare for the target recognition. The automatic target clustering is one of the most difficult tasks in the SAR-image automatic target recognition system. The density-based spatial clustering of applications with noise (DBSCAN) relies on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN was first used in the SAR image processing, which has many excellent features: only two insensitivity parameters (radius of neighborhood and minimum number of points) are needed; clusters of arbitrary shapes which fit in with the coarse detected SAR images can be discovered; and the calculation time and memory can be reduced. In the multi-feature ROI discrimination scheme, we extract several target features which contain the geometry features such as the area discriminator and Radon-transform based target profile discriminator, the distribution characteristics such as the EFF discriminator, and the EM scattering property such as the PPR discriminator. The synthesized judgment effectively eliminates the false alarms.

  8. Combined imaging of oxidative stress and microscopic structure reveals new features in human atherosclerotic plaques

    NASA Astrophysics Data System (ADS)

    Lilledahl, Magnus B.; Gustafsson, Håkan; Ellingsen, Pål Gunnar; Zachrisson, Helene; Hallbeck, Martin; Hagen, Vegard Stenhjem; Kildemo, Morten; Lindgren, Mikael

    2015-02-01

    Human atherosclerotic samples collected by carotid endarterectomy were investigated using electronic paramagnetic resonance imaging (EPRI) for visualization of reactive oxygen species, and nonlinear optical microscopy (NLOM) to study structural features. Regions of strong EPRI signal, indicating a higher concentration of reactive oxygen species and increased inflammation, were found to colocalize with regions dense in cholesterol crystals as revealed by NLOM.

  9. A Feature-based Approach to Big Data Analysis of Medical Images

    PubMed Central

    Toews, Matthew; Wachinger, Christian; Estepar, Raul San Jose; Wells, William M.

    2015-01-01

    This paper proposes an inference method well-suited to large sets of medical images. The method is based upon a framework where distinctive 3D scale-invariant features are indexed efficiently to identify approximate nearest-neighbor (NN) feature matches in O(log N) computational complexity in the number of images N. It thus scales well to large data sets, in contrast to methods based on pair-wise image registration or feature matching requiring O(N) complexity. Our theoretical contribution is a density estimator based on a generative model that generalizes kernel density estimation and K-nearest neighbor (KNN) methods. The estimator can be used for on-the-fly queries, without requiring explicit parametric models or an off-line training phase. The method is validated on a large multi-site data set of 95,000,000 features extracted from 19,000 lung CT scans. Subject-level classification identifies all images of the same subjects across the entire data set despite deformation due to breathing state, including unintentional duplicate scans. State-of-the-art performance is achieved in predicting chronic pulmonary obstructive disorder (COPD) severity across the 5-category GOLD clinical rating, with an accuracy of 89% if both exact and one-off predictions are considered correct. PMID:26221685

  10. Histological Image Processing Features Induce a Quantitative Characterization of Chronic Tumor Hypoxia

    PubMed Central

    Grabocka, Elda; Bar-Sagi, Dafna; Mishra, Bud

    2016-01-01

    Hypoxia in tumors signifies resistance to therapy. Despite a wealth of tumor histology data, including anti-pimonidazole staining, no current methods use these data to induce a quantitative characterization of chronic tumor hypoxia in time and space. We use image-processing algorithms to develop a set of candidate image features that can formulate just such a quantitative description of xenographed colorectal chronic tumor hypoxia. Two features in particular give low-variance measures of chronic hypoxia near a vessel: intensity sampling that extends radially away from approximated blood vessel centroids, and multithresholding to segment tumor tissue into normal, hypoxic, and necrotic regions. From these features we derive a spatiotemporal logical expression whose truth value depends on its predicate clauses that are grounded in this histological evidence. As an alternative to the spatiotemporal logical formulation, we also propose a way to formulate a linear regression function that uses all of the image features to learn what chronic hypoxia looks like, and then gives a quantitative similarity score once it is trained on a set of histology images. PMID:27093539

  11. A Feature-Based Approach to Big Data Analysis of Medical Images.

    PubMed

    Toews, Matthew; Wachinger, Christian; Estepar, Raul San Jose; Wells, William M

    2015-01-01

    This paper proposes an inference method well-suited to large sets of medical images. The method is based upon a framework where distinctive 3D scale-invariant features are indexed efficiently to identify approximate nearest-neighbor (NN) feature matches-in O (log N) computational complexity in the number of images N. It thus scales well to large data sets, in contrast to methods based on pair-wise image registration or feature matching requiring O(N) complexity. Our theoretical contribution is a density estimator based on a generative model that generalizes kernel density estimation and K-nearest neighbor (KNN) methods.. The estimator can be used for on-the-fly queries, without requiring explicit parametric models or an off-line training phase. The method is validated on a large multi-site data set of 95,000,000 features extracted from 19,000 lung CT scans. Subject-level classification identifies all images of the same subjects across the entire data set despite deformation due to breathing state, including unintentional duplicate scans. State-of-the-art performance is achieved in predicting chronic pulmonary obstructive disorder (COPD) severity across the 5-category GOLD clinical rating, with an accuracy of 89% if both exact and one-off predictions are considered correct. PMID:26221685

  12. Scale profile as feature for quick satellite image object-based classification

    NASA Astrophysics Data System (ADS)

    Dubois, David; Lepage, Richard

    2013-05-01

    With the increasing precision of recent spaceborne sensors, remotely sensed images have become exceedingly large. These images are being used more and more often in the preparation of emergency maps when a disaster occurs. Visual interpretation of these images is long and automatic pixel-based methods require a lot of memory, processing power and time. In this paper, we propose to use a fast level-set image transformation in order to obtain a hierarchical representation of image's objects. A scale profile is then extracted and included as a relevant feature for land-use classification in urban areas. The main contribution of this paper is the analysis of the scale profile for remote sensing applications. The data set from the earthquake that occurred on 12 January 2012 in Haiti is used.

  13. Combining multispectral images and selected textural features from high-resolution images to improve discrimination of forest canopies

    NASA Astrophysics Data System (ADS)

    Ruiz, Luis A.; Inan, Igor; Baridon, Juan E.; Lanfranco, Jorge W.

    1998-12-01

    Discrimination of vegetation canopies for production of forestry and land use thematic cartography from multispectral satellite images requires high spectral and spatial resolutions, usually not available in this type of images. A methodology is proposed to improve a vegetation oriented classification from a Landsat TM image by adding texture information obtained from panchromatic aerial photographs. Multispectral classification was used to create a mask of the forested areas that was applied over the aerial mosaic composition. Further vegetation classes were defined based on textural differences, and eight texture features derived from the gray level co-occurrence matrix, three textural energy indicators and a factor of edgeness were tested. A selection of optimal features and textural parameters such as number of gray levels, window size and distance between pixels was performed using principal components and stepwise discriminant analysis techniques with a set of representative samples from each class. After a texture segmentation of panchromatic aerial imagery using optimal parameters and features was completed, a post-classification process based on morphological operations was applied to avoid the neighboring effect generated by the texture analysis. Overall accuracy in the identification of texture classes using the four best feathers was 86.6%, while the 88% of accuracy was achieved in the classification of the complete image. This method is useful for discrimination of certain vegetation classes with low spectral separability and arranged in small forest units, increasing the classification detail in those areas of particular interest.

  14. Comparison of the effectiveness of alternative feature sets in shape retrieval of multicomponent images

    NASA Astrophysics Data System (ADS)

    Eakins, John P.; Edwards, Jonathan D.; Riley, K. Jonathan; Rosin, Paul L.

    2001-01-01

    Many different kinds of features have been used as the basis for shape retrieval from image databases. This paper investigates the relative effectiveness of several types of global shape feature, both singly and in combination. The features compared include well-established descriptors such as Fourier coefficients and moment invariants, as well as recently-proposed measures of triangularity and ellipticity. Experiments were conducted within the framework of the ARTISAN shape retrieval system, and retrieval effectiveness assessed on a database of over 10,000 images, using 24 queries and associated ground truth supplied by the UK Patent Office . Our experiments revealed only minor differences in retrieval effectiveness between different measures, suggesting that a wide variety of shape feature combinations can provide adequate discriminating power for effective shape retrieval in multi-component image collections such as trademark registries. Marked differences between measures were observed for some individual queries, suggesting that there could be considerable scope for improving retrieval effectiveness by providing users with an improved framework for searching multi-dimensional feature space.

  15. Comparison of the effectiveness of alternative feature sets in shape retrieval of multicomponent images

    NASA Astrophysics Data System (ADS)

    Eakins, John P.; Edwards, Jonathan D.; Riley, K. Jonathan; Rosin, Paul L.

    2000-12-01

    Many different kinds of features have been used as the basis for shape retrieval from image databases. This paper investigates the relative effectiveness of several types of global shape feature, both singly and in combination. The features compared include well-established descriptors such as Fourier coefficients and moment invariants, as well as recently-proposed measures of triangularity and ellipticity. Experiments were conducted within the framework of the ARTISAN shape retrieval system, and retrieval effectiveness assessed on a database of over 10,000 images, using 24 queries and associated ground truth supplied by the UK Patent Office . Our experiments revealed only minor differences in retrieval effectiveness between different measures, suggesting that a wide variety of shape feature combinations can provide adequate discriminating power for effective shape retrieval in multi-component image collections such as trademark registries. Marked differences between measures were observed for some individual queries, suggesting that there could be considerable scope for improving retrieval effectiveness by providing users with an improved framework for searching multi-dimensional feature space.

  16. Ear feature region detection based on a combined image segmentation algorithm-KRM

    NASA Astrophysics Data System (ADS)

    Jiang, Jingying; Zhang, Hao; Zhang, Qi; Lu, Junsheng; Ma, Zhenhe; Xu, Kexin

    2014-02-01

    Scale Invariant Feature Transform SIFT algorithm is widely used for ear feature matching and recognition. However, the application of the algorithm is usually interfered by the non-target areas within the whole image, and the interference would then affect the matching and recognition of ear features. To solve this problem, a combined image segmentation algorithm i.e. KRM was introduced in this paper, As the human ear recognition pretreatment method. Firstly, the target areas of ears were extracted by the KRM algorithm and then SIFT algorithm could be applied to the detection and matching of features. The present KRM algorithm follows three steps: (1)the image was preliminarily segmented into foreground target area and background area by using K-means clustering algorithm; (2)Region growing method was used to merge the over-segmented areas; (3)Morphology erosion filtering method was applied to obtain the final segmented regions. The experiment results showed that the KRM method could effectively improve the accuracy and robustness of ear feature matching and recognition based on SIFT algorithm.

  17. Biomedical article retrieval using multimodal features and image annotations in region-based CBIR

    NASA Astrophysics Data System (ADS)

    You, Daekeun; Antani, Sameer; Demner-Fushman, Dina; Rahman, Md Mahmudur; Govindaraju, Venu; Thoma, George R.

    2010-01-01

    Biomedical images are invaluable in establishing diagnosis, acquiring technical skills, and implementing best practices in many areas of medicine. At present, images needed for instructional purposes or in support of clinical decisions appear in specialized databases and in biomedical articles, and are often not easily accessible to retrieval tools. Our goal is to automatically annotate images extracted from scientific publications with respect to their usefulness for clinical decision support and instructional purposes, and project the annotations onto images stored in databases by linking images through content-based image similarity. Authors often use text labels and pointers overlaid on figures and illustrations in the articles to highlight regions of interest (ROI). These annotations are then referenced in the caption text or figure citations in the article text. In previous research we have developed two methods (a heuristic and dynamic time warping-based methods) for localizing and recognizing such pointers on biomedical images. In this work, we add robustness to our previous efforts by using a machine learning based approach to localizing and recognizing the pointers. Identifying these can assist in extracting relevant image content at regions within the image that are likely to be highly relevant to the discussion in the article text. Image regions can then be annotated using biomedical concepts from extracted snippets of text pertaining to images in scientific biomedical articles that are identified using National Library of Medicine's Unified Medical Language System® (UMLS) Metathesaurus. The resulting regional annotation and extracted image content are then used as indices for biomedical article retrieval using the multimodal features and region-based content-based image retrieval (CBIR) techniques. The hypothesis that such an approach would improve biomedical document retrieval is validated through experiments on an expert-marked biomedical article

  18. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features

    PubMed Central

    Yu, Kun-Hsing; Zhang, Ce; Berry, Gerald J.; Altman, Russ B.; Ré, Christopher; Rubin, Daniel L.; Snyder, Michael

    2016-01-01

    Lung cancer is the most prevalent cancer worldwide, and histopathological assessment is indispensable for its diagnosis. However, human evaluation of pathology slides cannot accurately predict patients' prognoses. In this study, we obtain 2,186 haematoxylin and eosin stained histopathology whole-slide images of lung adenocarcinoma and squamous cell carcinoma patients from The Cancer Genome Atlas (TCGA), and 294 additional images from Stanford Tissue Microarray (TMA) Database. We extract 9,879 quantitative image features and use regularized machine-learning methods to select the top features and to distinguish shorter-term survivors from longer-term survivors with stage I adenocarcinoma (P<0.003) or squamous cell carcinoma (P=0.023) in the TCGA data set. We validate the survival prediction framework with the TMA cohort (P<0.036 for both tumour types). Our results suggest that automatically derived image features can predict the prognosis of lung cancer patients and thereby contribute to precision oncology. Our methods are extensible to histopathology images of other organs. PMID:27527408

  19. Large scale near-duplicate celebrity web images retrieval using visual and textual features.

    PubMed

    Qiao, Fengcai; Wang, Cheng; Zhang, Xin; Wang, Hui

    2013-01-01

    Near-duplicate image retrieval is a classical research problem in computer vision toward many applications such as image annotation and content-based image retrieval. On the web, near-duplication is more prevalent in queries for celebrities and historical figures which are of particular interest to the end users. Existing methods such as bag-of-visual-words (BoVW) solve this problem mainly by exploiting purely visual features. To overcome this limitation, this paper proposes a novel text-based data-driven reranking framework, which utilizes textual features and is combined with state-of-art BoVW schemes. Under this framework, the input of the retrieval procedure is still only a query image. To verify the proposed approach, a dataset of 2 million images of 1089 different celebrities together with their accompanying texts is constructed. In addition, we comprehensively analyze the different categories of near duplication observed in our constructed dataset. Experimental results on this dataset show that the proposed framework can achieve higher mean average precision (mAP) with an improvement of 21% on average in comparison with the approaches based only on visual features, while does not notably prolong the retrieval time.

  20. Hepatic involvement in HELLP syndrome: an update with emphasis on imaging features.

    PubMed

    Perronne, Laetitia; Dohan, Anthony; Bazeries, Paul; Guerrache, Youcef; Fohlen, Audrey; Rousset, Pascal; Aubé, Christophe; Laurent, Valérie; Morel, Olivier; Boudiaf, Mourad; Hoeffel, Christine; Soyer, Philippe

    2015-10-01

    HELLP syndrome, which consists of hemolysis, elevated liver enzymes, and low platelet count is an unusual complication of pregnancy that is observed in only 10% to 15% of women with preeclampsia. Hepatic involvement in HELLP syndrome may present with various imaging features depending on the specific condition that includes nonspecific abnormalities such as perihepatic free fluid, hepatic steatosis, liver enlargement, and periportal halo that may precede more severe conditions such as hepatic hematoma and hepatic rupture with hemoperitoneum. Maternal clinical symptoms may be nonspecific and easily mistaken for a variety of other conditions that should be recognized. Because hepatic hematoma occurring in association with preeclampsia and HELLP syndrome is a potentially life-threatening complication, prompt depiction is critical and may help reduce morbidity and mortality. This review provides an update on demographics, risk factors, pathophysiology, and clinical features of hepatic complications due to HELLP syndrome along with a special emphasis on the imaging features of these uncommon conditions. PMID:26099472

  1. The linear attenuation coefficients as features of multiple energy CT image classification

    NASA Astrophysics Data System (ADS)

    Homem, M. R. P.; Mascarenhas, N. D. A.; Cruvinel, P. E.

    2000-09-01

    We present in this paper an analysis of the linear attenuation coefficients as useful features of single and multiple energy CT images with the use of statistical pattern classification tools. We analyzed four CT images through two pointwise classifiers (the first classifier is based on the maximum-likelihood criterion and the second classifier is based on the k-means clustering algorithm) and one contextual Bayesian classifier (ICM algorithm - Iterated Conditional Modes) using an a priori Potts-Strauss model. A feature extraction procedure using the Jeffries-Matusita (J-M) distance and the Karhunen-Loève transformation was also performed. Both the classification and the feature selection procedures were found to be in agreement with the predicted discrimination given by the separation of the linear attenuation coefficient curves for different materials.

  2. Nonlinear feature extraction using kernel principal component analysis with non-negative pre-image.

    PubMed

    Kallas, Maya; Honeine, Paul; Richard, Cedric; Amoud, Hassan; Francis, Clovis

    2010-01-01

    The inherent physical characteristics of many real-life phenomena, including biological and physiological aspects, require adapted nonlinear tools. Moreover, the additive nature in some situations involve solutions expressed as positive combinations of data. In this paper, we propose a nonlinear feature extraction method, with a non-negativity constraint. To this end, the kernel principal component analysis is considered to define the most relevant features in the reproducing kernel Hilbert space. These features are the nonlinear principal components with high-order correlations between input variables. A pre-image technique is required to get back to the input space. With a non-negative constraint, we show that one can solve the pre-image problem efficiently, using a simple iterative scheme. Furthermore, the constrained solution contributes to the stability of the algorithm. Experimental results on event-related potentials (ERP) illustrate the efficiency of the proposed method.

  3. Feature discrimination and detection probability in synthetic aperture radar imaging system

    NASA Technical Reports Server (NTRS)

    Lipes, R. G.; Butman, S. A.

    1977-01-01

    Images obtained using synthetic aperture radar (SAR) systems can only represent the intensities of resolution cells in the scene of interest probabilistically since radar receiver noise and Rayleigh scattering of the transmitted radiation are always present. Consequently, when features to be identified differ only by their contribution to the mean power of the radar return, discrimination can be treated by detection theory. In this paper, we develop a 'sufficient statistic' for discriminating between competing features and compare it with some suboptimal methods frequently used. Discrimination is measured by probability of detection error and depends on number of samples or 'looks', signal-to-noise ratio (SNR), and ratio of mean power returns from the competing features. Our results show discrimination and image quality rapidly saturate with SNR (very small improvement for SNR not less than 10 dB) but continue to improve with increasing number of looks.

  4. A feature-based image watermarking scheme robust to local geometrical distortions

    NASA Astrophysics Data System (ADS)

    Wang, Xiang-yang; Hou, Li-min; Yang, Hong-ying

    2009-06-01

    Geometric attacks are the Achilles heel for many image watermarking schemes. Geometric attacks can be decomposed into two classes: global affine transforms and local geometrical distortions. Most countermeasures proposed in the literature only address the problem of global affine transforms (e.g. rotation, scaling and translation). In this paper, we propose a blind image watermarking algorithm robust to local geometrical distortions such as row or column removal, cropping, local random bend, etc. The robust feature points are adaptively extracted from digital images and local image regions (circular regions) that are invariant to geometric attacks are obtained according to the multi-scale space representation and image normalization. At each local image region, the watermark is embedded by quantizing the magnitudes of the pseudo-Zernike moments. By binding digital watermark with local image regions, resilience against local geometrical distortions can be readily obtained. Experimental results show that the proposed image watermarking is not only invisible and robust against common image processing operations, such as sharpening, noise adding, JPEG compression, etc, but also robust against geometric attacks such as rotation, translation, scaling, row or column removal, copping, local random bend, etc.

  5. TS-MRF sonar image segmentation based on the levels feature information

    NASA Astrophysics Data System (ADS)

    Wu, Tao; Xia, Ping; Liu, Xiaomei; Lei, Bangjun

    2015-12-01

    According to traditional methods of image segmentation on sonar image processing with less robustness and the problem of low accuracy, we propose the method of sonar image segmentation based on Tree-Structured Markov Random Field(TS-MRF), the algorithm shows better ability in using spatial information. First, using a tree structure constraint two-valued MRF sequences to model sonar image, through the node to describe local information of image, hierarchy information establish interconnected relationships through nodes, at the same time when we describe the hierarchical structure information of the image, we can preserve an image's local information effectively. Then, we define split gain coefficients to reflect the ratio that marking posterior probability division before and after the splitting on the assumption of the known image viewing features, and viewing gain coefficients of judgment as the basis for determining binary tree of node split to reduce the complexity of solving a posterior probability. Finally, during the process of image segmentation, continuing to split the leaf nodes with the maximum splitting gain, so we can get the splitting results. We add merge during the process of segmentation. Using the methods of region splitting and merging to reduce the error division, so we can obtain the final segmentation results. Experimental results show that this approach has high segmentation accuracy and robustness.

  6. Color image quality assessment with biologically inspired feature and machine learning

    NASA Astrophysics Data System (ADS)

    Deng, Cheng; Tao, Dacheng

    2010-07-01

    In this paper, we present a new no-reference quality assessment metric for color images by using biologically inspired features (BIFs) and machine learning. In this metric, we first adopt a biologically inspired model to mimic the visual cortex and represent a color image based on BIFs which unifies color units, intensity units and C1 units. Then, in order to reduce the complexity and benefit the classification, the high dimensional features are projected to a low dimensional representation with manifold learning. Finally, a multiclass classification process is performed on this new low dimensional representation of the image and the quality assessment is based on the learned classification result in order to respect the one of the human observers. Instead of computing a final note, our method classifies the quality according to the quality scale recommended by the ITU. The preliminary results show that the developed metric can achieve good quality evaluation performance.

  7. Digital Image Forgery Detection Using JPEG Features and Local Noise Discrepancies

    PubMed Central

    Liu, Bo; Pun, Chi-Man; Yuan, Xiao-Chen

    2014-01-01

    Wide availability of image processing software makes counterfeiting become an easy and low-cost way to distort or conceal facts. Driven by great needs for valid forensic technique, many methods have been proposed to expose such forgeries. In this paper, we proposed an integrated algorithm which was able to detect two commonly used fraud practices: copy-move and splicing forgery in digital picture. To achieve this target, a special descriptor for each block was created combining the feature from JPEG block artificial grid with that from noise estimation. And forehand image quality assessment procedure reconciled these different features by setting proper weights. Experimental results showed that, compared to existing algorithms, our proposed method is effective on detecting both copy-move and splicing forgery regardless of JPEG compression ratio of the input image. PMID:24955389

  8. Diagnostic imaging of psoriatic arthritis. Part I: etiopathogenesis, classifications and radiographic features

    PubMed Central

    Matuszewska, Genowefa; Kwiatkowska, Brygida; Pracoń, Grzegorz

    2016-01-01

    Psoriatic arthritis is one of the spondyloarthritis. It is a disease of clinical heterogenicity, which may affect peripheral joints, as well as axial spine, with presence of inflammatory lesions in soft tissue, in a form of dactylitis and enthesopathy. Plain radiography remains the basic imaging modality for PsA diagnosis, although early inflammatory changes affecting soft tissue and bone marrow cannot be detected with its use, or the image is indistinctive. Typical radiographic features of PsA occur in an advanced disease, mainly within the synovial joints, but also in fibrocartilaginous joints, such as sacroiliac joints, and additionally in entheses of tendons and ligaments. Moll and Wright classified PsA into 5 subtypes: asymmetric oligoarthritis, symmetric polyarthritis, arthritis mutilans, distal interphalangeal arthritis of the hands and feet and spinal column involvement. In this part of the paper we discuss radiographic features of the disease. The next one will address magnetic resonance imaging and ultrasonography. PMID:27104004

  9. Classification of underground pipe scanned images using feature extraction and neuro-fuzzy algorithm.

    PubMed

    Sinha, S K; Karray, F

    2002-01-01

    Pipeline surface defects such as holes and cracks cause major problems for utility managers, particularly when the pipeline is buried under the ground. Manual inspection for surface defects in the pipeline has a number of drawbacks, including subjectivity, varying standards, and high costs. Automatic inspection system using image processing and artificial intelligence techniques can overcome many of these disadvantages and offer utility managers an opportunity to significantly improve quality and reduce costs. A recognition and classification of pipe cracks using images analysis and neuro-fuzzy algorithm is proposed. In the preprocessing step the scanned images of pipe are analyzed and crack features are extracted. In the classification step the neuro-fuzzy algorithm is developed that employs a fuzzy membership function and error backpropagation algorithm. The idea behind the proposed approach is that the fuzzy membership function will absorb variation of feature values and the backpropagation network, with its learning ability, will show good classification efficiency.

  10. Constructing New Biorthogonal Wavelet Type which Matched for Extracting the Iris Image Features

    NASA Astrophysics Data System (ADS)

    Rizal Isnanto, R.; Suhardjo; Susanto, Adhi

    2013-04-01

    Some former research have been made for obtaining a new type of wavelet. In case of iris recognition using orthogonal or biorthogonal wavelets, it had been obtained that Haar filter is most suitable to recognize the iris image. However, designing the new wavelet should be done to find a most matched wavelet to extract the iris image features, for which we can easily apply it for identification, recognition, or authentication purposes. In this research, a new biorthogonal wavelet was designed based on Haar filter properties and Haar's orthogonality conditions. As result, it can be obtained a new biorthogonal 5/7 filter type wavelet which has a better than other types of wavelets, including Haar, to extract the iris image features based on its mean-squared error (MSE) and Euclidean distance parameters.

  11. Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images.

    PubMed

    Mookiah, Muthu Rama Krishnan; Acharya, U Rajendra; Koh, Joel E W; Chandran, Vinod; Chua, Chua Kuang; Tan, Jen Hong; Lim, Choo Min; Ng, E Y K; Noronha, Kevin; Tong, Louis; Laude, Augustinus

    2014-10-01

    Age-related Macular Degeneration (AMD) is one of the major causes of vision loss and blindness in ageing population. Currently, there is no cure for AMD, however early detection and subsequent treatment may prevent the severe vision loss or slow the progression of the disease. AMD can be classified into two types: dry and wet AMDs. The people with macular degeneration are mostly affected by dry AMD. Early symptoms of AMD are formation of drusen and yellow pigmentation. These lesions are identified by manual inspection of fundus images by the ophthalmologists. It is a time consuming, tiresome process, and hence an automated diagnosis of AMD screening tool can aid clinicians in their diagnosis significantly. This study proposes an automated dry AMD detection system using various entropies (Shannon, Kapur, Renyi and Yager), Higher Order Spectra (HOS) bispectra features, Fractional Dimension (FD), and Gabor wavelet features extracted from greyscale fundus images. The features are ranked using t-test, Kullback-Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance (CBBD), Receiver Operating Characteristics (ROC) curve-based and Wilcoxon ranking methods in order to select optimum features and classified into normal and AMD classes using Naive Bayes (NB), k-Nearest Neighbour (k-NN), Probabilistic Neural Network (PNN), Decision Tree (DT) and Support Vector Machine (SVM) classifiers. The performance of the proposed system is evaluated using private (Kasturba Medical Hospital, Manipal, India), Automated Retinal Image Analysis (ARIA) and STructured Analysis of the Retina (STARE) datasets. The proposed system yielded the highest average classification accuracies of 90.19%, 95.07% and 95% with 42, 54 and 38 optimal ranked features using SVM classifier for private, ARIA and STARE datasets respectively. This automated AMD detection system can be used for mass fundus image screening and aid clinicians by making better use of their expertise on selected images that

  12. Methodology for classification of geographical features with remote sensing images: Application to tidal flats

    NASA Astrophysics Data System (ADS)

    Revollo Sarmiento, G. N.; Cipolletti, M. P.; Perillo, M. M.; Delrieux, C. A.; Perillo, Gerardo M. E.

    2016-03-01

    Tidal flats generally exhibit ponds of diverse size, shape, orientation and origin. Studying the genesis, evolution, stability and erosive mechanisms of these geographic features is critical to understand the dynamics of coastal wetlands. However, monitoring these locations through direct access is hard and expensive, not always feasible, and environmentally damaging. Processing remote sensing images is a natural alternative for the extraction of qualitative and quantitative data due to their non-invasive nature. In this work, a robust methodology for automatic classification of ponds and tidal creeks in tidal flats using Google Earth images is proposed. The applicability of our method is tested in nine zones with different morphological settings. Each zone is processed by a segmentation stage, where ponds and tidal creeks are identified. Next, each geographical feature is measured and a set of shape descriptors is calculated. This dataset, together with a-priori classification of each geographical feature, is used to define a regression model, which allows an extensive automatic classification of large volumes of data discriminating ponds and tidal creeks against other various geographical features. In all cases, we identified and automatically classified different geographic features with an average accuracy over 90% (89.7% in the worst case, and 99.4% in the best case). These results show the feasibility of using freely available Google Earth imagery for the automatic identification and classification of complex geographical features. Also, the presented methodology may be easily applied in other wetlands of the world and perhaps employing other remote sensing imagery.

  13. Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features

    NASA Astrophysics Data System (ADS)

    Du, Peijun; Samat, Alim; Waske, Björn; Liu, Sicong; Li, Zhenhong

    2015-07-01

    Fully Polarimetric Synthetic Aperture Radar (PolSAR) has the advantages of all-weather, day and night observation and high resolution capabilities. The collected data are usually sorted in Sinclair matrix, coherence or covariance matrices which are directly related to physical properties of natural media and backscattering mechanism. Additional information related to the nature of scattering medium can be exploited through polarimetric decomposition theorems. Accordingly, PolSAR image classification gains increasing attentions from remote sensing communities in recent years. However, the above polarimetric measurements or parameters cannot provide sufficient information for accurate PolSAR image classification in some scenarios, e.g. in complex urban areas where different scattering mediums may exhibit similar PolSAR response due to couples of unavoidable reasons. Inspired by the complementarity between spectral and spatial features bringing remarkable improvements in optical image classification, the complementary information between polarimetric and spatial features may also contribute to PolSAR image classification. Therefore, the roles of textural features such as contrast, dissimilarity, homogeneity and local range, morphological profiles (MPs) in PolSAR image classification are investigated using two advanced ensemble learning (EL) classifiers: Random Forest and Rotation Forest. Supervised Wishart classifier and support vector machines (SVMs) are used as benchmark classifiers for the evaluation and comparison purposes. Experimental results with three Radarsat-2 images in quad polarization mode indicate that classification accuracies could be significantly increased by integrating spatial and polarimetric features using ensemble learning strategies. Rotation Forest can get better accuracy than SVM and Random Forest, in the meantime, Random Forest is much faster than Rotation Forest.

  14. Semi-Automatic Normalization of Multitemporal Remote Images Based on Vegetative Pseudo-Invariant Features

    PubMed Central

    Garcia-Torres, Luis; Caballero-Novella, Juan J.; Gómez-Candón, David; De-Castro, Ana Isabel

    2014-01-01

    A procedure to achieve the semi-automatic relative image normalization of multitemporal remote images of an agricultural scene called ARIN was developed using the following procedures: 1) defining the same parcel of selected vegetative pseudo-invariant features (VPIFs) in each multitemporal image; 2) extracting data concerning the VPIF spectral bands from each image; 3) calculating the correction factors (CFs) for each image band to fit each image band to the average value of the image series; and 4) obtaining the normalized images by linear transformation of each original image band through the corresponding CF. ARIN software was developed to semi-automatically perform the ARIN procedure. We have validated ARIN using seven GeoEye-1 satellite images taken over the same location in Southern Spain from early April to October 2010 at an interval of approximately 3 to 4 weeks. The following three VPIFs were chosen: citrus orchards (CIT), olive orchards (OLI) and poplar groves (POP). In the ARIN-normalized images, the range, standard deviation (s. d.) and root mean square error (RMSE) of the spectral bands and vegetation indices were considerably reduced compared to the original images, regardless of the VPIF or the combination of VPIFs selected for normalization, which demonstrates the method’s efficacy. The correlation coefficients between the CFs among VPIFs for any spectral band (and all bands overall) were calculated to be at least 0.85 and were significant at P = 0.95, indicating that the normalization procedure was comparably performed regardless of the VPIF chosen. ARIN method was designed only for agricultural and forestry landscapes where VPIFs can be identified. PMID:24604031

  15. Investigation of automated feature extraction techniques for applications in cancer detection from multispectral histopathology images

    NASA Astrophysics Data System (ADS)

    Harvey, Neal R.; Levenson, Richard M.; Rimm, David L.

    2003-05-01

    Recent developments in imaging technology mean that it is now possible to obtain high-resolution histological image data at multiple wavelengths. This allows pathologists to image specimens over a full spectrum, thereby revealing (often subtle) distinctions between different types of tissue. With this type of data, the spectral content of the specimens, combined with quantitative spatial feature characterization may make it possible not only to identify the presence of an abnormality, but also to classify it accurately. However, such are the quantities and complexities of these data, that without new automated techniques to assist in the data analysis, the information contained in the data will remain inaccessible to those who need it. We investigate the application of a recently developed system for the automated analysis of multi-/hyper-spectral satellite image data to the problem of cancer detection from multispectral histopathology image data. The system provides a means for a human expert to provide training data simply by highlighting regions in an image using a computer mouse. Application of these feature extraction techniques to examples of both training and out-of-training-sample data demonstrate that these, as yet unoptimized, techniques already show promise in the discrimination between benign and malignant cells from a variety of samples.

  16. Automatic registration of Iphone images to LASER point clouds of the urban structures using shape features

    NASA Astrophysics Data System (ADS)

    Sirmacek, B.; Lindenbergh, R. C.; Menenti, M.

    2013-10-01

    Fusion of 3D airborne laser (LIDAR) data and terrestrial optical imagery can be applied in 3D urban modeling and model up-dating. The most challenging aspect of the fusion procedure is registering the terrestrial optical images on the LIDAR point clouds. In this article, we propose an approach for registering these two different data from different sensor sources. As we use iPhone camera images which are taken in front of the interested urban structure by the application user and the high resolution LIDAR point clouds of the acquired by an airborne laser sensor. After finding the photo capturing position and orientation from the iPhone photograph metafile, we automatically select the area of interest in the point cloud and transform it into a range image which has only grayscale intensity levels according to the distance from the image acquisition position. We benefit from local features for registering the iPhone image to the generated range image. In this article, we have applied the registration process based on local feature extraction and graph matching. Finally, the registration result is used for facade texture mapping on the 3D building surface mesh which is generated from the LIDAR point cloud. Our experimental results indicate possible usage of the proposed algorithm framework for 3D urban map updating and enhancing purposes.

  17. Invasive atypical thymic carcinoid: three case reports and literature review

    PubMed Central

    Zhu, Shan; Wang, Zhong-Tang; Liu, Wen-Zhi; Zong, Shi-Xiang; Li, Bao-Sheng

    2016-01-01

    Atypical thymic carcinoid is an extremely rare thymic neuroendocrine tumor derived from the neuroendocrine system. The aims of this paper were to investigate the clinical features of atypical thymic carcinoid and collate information and experience to improve the diagnosis and treatment of this disease. We describe three cases of atypical carcinoid of the thymus; clinical features, pathological data, treatment modalities, and short-term patient outcomes were summarized and analyzed. The initial clinical symptoms and signs of all three patients were nonspecific and an anterior mediastinal mass was found in each patient on chest computed tomography scan. All three patients underwent surgical resection (total thymectomy and complete excision of the tumor), followed by postoperative radiotherapy, with or without chemotherapy. The diagnoses of three patients were confirmed by pathological and immunohistochemical evaluation. We also present a review of the literature to collate as much information as possible and provide a reference for proper diagnosis and treatment of atypical thyroid carcinoid. PMID:27785065

  18. Feature Visibility Limits in the Non-Linear Enhancement of Turbid Images

    NASA Technical Reports Server (NTRS)

    Jobson, Daniel J.; Rahman, Zia-ur; Woodell, Glenn A.

    2003-01-01

    The advancement of non-linear processing methods for generic automatic clarification of turbid imagery has led us from extensions of entirely passive multiscale Retinex processing to a new framework of active measurement and control of the enhancement process called the Visual Servo. In the process of testing this new non-linear computational scheme, we have identified that feature visibility limits in the post-enhancement image now simplify to a single signal-to-noise figure of merit: a feature is visible if the feature-background signal difference is greater than the RMS noise level. In other words, a signal-to-noise limit of approximately unity constitutes a lower limit on feature visibility.

  19. Identification of cancerous gastric cells based on common features extracted from hyperspectral microscopic images

    PubMed Central

    Zhu, Siqi; Su, Kang; Liu, Yumeng; Yin, Hao; Li, Zhen; Huang, Furong; Chen, Zhenqiang; Chen, Weidong; Zhang, Ge; Chen, Yihong

    2015-01-01

    We construct a microscopic hyperspectral imaging system to distinguish between normal and cancerous gastric cells. We study common transmission-spectra features that only emerge when the samples are dyed with hematoxylin and eosin (H&E) stain. Subsequently, we classify the obtained visible-range transmission spectra of the samples into three zones. Distinct features are observed in the spectral responses between the normal and cancerous cell nuclei in each zone, which depend on the pH level of the cell nucleus. Cancerous gastric cells are precisely identified according to these features. The average cancer-cell identification accuracy obtained with a backpropagation algorithm program trained with these features is 95%. PMID:25909000

  20. Frequency response functions of shape features from full-field vibration measurements using digital image correlation

    NASA Astrophysics Data System (ADS)

    Wang, Weizhuo; Mottershead, John E.; Siebert, Thorsten; Pipino, Andrea

    2012-04-01

    The availability of high speed digital cameras has enabled three-dimensional (3D) vibration measurement by stereography and digital image correlation (DIC). The 3D DIC technique provides non-contact full-field measurements on complex surfaces whereas conventional modal testing methods employ point-wise frequency response functions. It is proposed to identify the modal properties by utilising the domain-wise responses captured by a DIC system. This idea will be illustrated by a case study in the form a car bonnet of 3D irregular shape typical of many engineering structures. The full-field measured data are highly redundant, but the application of image processing using functional transformation enables the extraction of a small number of shape features without any significant loss of information from the raw DIC data. The complex bonnet surface on which the displacement responses are measured is essentially a 2-manifold. It is possible to apply surface parameterisation to 'flatten' the 3D surface to form a 2D planar domain. Well-developed image processing techniques are defined on planar domains and used to extract features from the displacement patterns on the surface of a specimen. An adaptive geometric moment descriptor (AGMD), defined on surface parametric space, is able to extract shape features from a series of full-field transient responses under random excitation. Results show the effectiveness of the AGMD and the obtained shape features are demonstrated to be succinct and efficient. Approximately 14 thousand data points of raw DIC measurement are represented by 20 shape feature terms at each time step. Shape-descriptor frequency response functions (SD-FRFs) of the response field and the loading field are derived in the shape feature space. It is seen that the SD-FRF has a similar format to the conventional receptance FRF. The usual modal identification procedure is applied to determine the natural frequencies, damping factors and eigen-shape-feature vectors

  1. Possibility Study of Scale Invariant Feature Transform (SIFT) Algorithm Application to Spine Magnetic Resonance Imaging

    PubMed Central

    Lee, Dong-Hoon; Lee, Do-Wan; Han, Bong-Soo

    2016-01-01

    The purpose of this study is an application of scale invariant feature transform (SIFT) algorithm to stitch the cervical-thoracic-lumbar (C-T-L) spine magnetic resonance (MR) images to provide a view of the entire spine in a single image. All MR images were acquired with fast spin echo (FSE) pulse sequence using two MR scanners (1.5 T and 3.0 T). The stitching procedures for each part of spine MR image were performed and implemented on a graphic user interface (GUI) configuration. Moreover, the stitching process is performed in two categories; manual point-to-point (mPTP) selection that performed by user specified corresponding matching points, and automated point-to-point (aPTP) selection that performed by SIFT algorithm. The stitched images using SIFT algorithm showed fine registered results and quantitatively acquired values also indicated little errors compared with commercially mounted stitching algorithm in MRI systems. Our study presented a preliminary validation of the SIFT algorithm application to MRI spine images, and the results indicated that the proposed approach can be performed well for the improvement of diagnosis. We believe that our approach can be helpful for the clinical application and extension of other medical imaging modalities for image stitching. PMID:27064404

  2. Segmentation and feature extraction of cervical spine x-ray images

    NASA Astrophysics Data System (ADS)

    Long, L. Rodney; Thoma, George R.

    1999-05-01

    As part of an R&D project in mixed text/image database design, the National Library of Medicine has archived a collection of 17,000 digitized x-ray images of the cervical and lumbar spine which were collected as part of the second National Health and Nutrition Examination Survey (NHANES II). To make this image data available and usable to a wide audience, we are investigating techniques for indexing the image content by automated or semi-automated means. Indexing of the images by features of interest to researchers in spine disease and structure requires effective segmentation of the vertebral anatomy. This paper describes work in progress toward this segmentation of the cervical spine images into anatomical components of interest, including anatomical landmarks for vertebral location, and segmentation and identification of individual vertebrae. Our work includes developing a reliable method for automatically fixing an anatomy-based coordinate system in the images, and work to adaptively threshold the images, using methods previously applied by researchers in cardioangiography. We describe the motivation for our work and present our current results in both areas.

  3. Atypical cases of Dowling–Degos disease

    PubMed Central

    Naveen, Kikkeri Narayanshetty; Athaniker, Sharatchandra B.; Hegde, Spandana P.; Shetty, Rahul; Radha, Hanumanthayya; Parinitha, Sadashivappa Sangam

    2016-01-01

    Dowling–Degos disease (DDD) is a rare autosomal dominant condition characterized by multiple, small, round pigmented macules usually arranged in reticular pattern, chiefly distributed in axillae and groins. Here we are reporting three atypical cases of DDD in a family. They had hypopigmented macules with typical features of DDD indicating generalized DDD. Histopathology confirmed the diagnosis. We present these three cases to stress the existence of generalized DDD phenotype in the Indian population. PMID:27057490

  4. Probability mapping of scarred myocardium using texture and intensity features in CMR images

    PubMed Central

    2013-01-01

    Background The myocardium exhibits heterogeneous nature due to scarring after Myocardial Infarction (MI). In Cardiac Magnetic Resonance (CMR) imaging, Late Gadolinium (LG) contrast agent enhances the intensity of scarred area in the myocardium. Methods In this paper, we propose a probability mapping technique using Texture and Intensity features to describe heterogeneous nature of the scarred myocardium in Cardiac Magnetic Resonance (CMR) images after Myocardial Infarction (MI). Scarred tissue and non-scarred tissue are represented with high and low probabilities, respectively. Intermediate values possibly indicate areas where the scarred and healthy tissues are interwoven. The probability map of scarred myocardium is calculated by using a probability function based on Bayes rule. Any set of features can be used in the probability function. Results In the present study, we demonstrate the use of two different types of features. One is based on the mean intensity of pixel and the other on underlying texture information of the scarred and non-scarred myocardium. Examples of probability maps computed using the mean intensity of pixel and the underlying texture information are presented. We hypothesize that the probability mapping of myocardium offers alternate visualization, possibly showing the details with physiological significance difficult to detect visually in the original CMR image. Conclusion The probability mapping obtained from the two features provides a way to define different cardiac segments which offer a way to identify areas in the myocardium of diagnostic importance (like core and border areas in scarred myocardium). PMID:24053280

  5. Human gait recognition by pyramid of HOG feature on silhouette images

    NASA Astrophysics Data System (ADS)

    Yang, Guang; Yin, Yafeng; Park, Jeanrok; Man, Hong

    2013-03-01

    As a uncommon biometric modality, human gait recognition has a great advantage of identify people at a distance without high resolution images. It has attracted much attention in recent years, especially in the fields of computer vision and remote sensing. In this paper, we propose a human gait recognition framework that consists of a reliable background subtraction method followed by the pyramid of Histogram of Gradient (pHOG) feature extraction on the silhouette image, and a Hidden Markov Model (HMM) based classifier. Through background subtraction, the silhouette of human gait in each frame is extracted and normalized from the raw video sequence. After removing the shadow and noise in each region of interest (ROI), pHOG feature is computed on the silhouettes images. Then the pHOG features of each gait class will be used to train a corresponding HMM. In the test stage, pHOG feature will be extracted from each test sequence and used to calculate the posterior probability toward each trained HMM model. Experimental results on the CASIA Gait Dataset B1 demonstrate that with our proposed method can achieve very competitive recognition rate.

  6. Computerized detection of unruptured aneurysms in MRA images: reduction of false positives using anatomical location features

    NASA Astrophysics Data System (ADS)

    Uchiyama, Yoshikazu; Gao, Xin; Hara, Takeshi; Fujita, Hiroshi; Ando, Hiromichi; Yamakawa, Hiroyasu; Asano, Takahiko; Kato, Hiroki; Iwama, Toru; Kanematsu, Masayuki; Hoshi, Hiroaki

    2008-03-01

    The detection of unruptured aneurysms is a major subject in magnetic resonance angiography (MRA). However, their accurate detection is often difficult because of the overlapping between the aneurysm and the adjacent vessels on maximum intensity projection images. The purpose of this study is to develop a computerized method for the detection of unruptured aneurysms in order to assist radiologists in image interpretation. The vessel regions were first segmented using gray-level thresholding and a region growing technique. The gradient concentration (GC) filter was then employed for the enhancement of the aneurysms. The initial candidates were identified in the GC image using a gray-level threshold. For the elimination of false positives (FPs), we determined shape features and an anatomical location feature. Finally, rule-based schemes and quadratic discriminant analysis were employed along with these features for distinguishing between the aneurysms and the FPs. The sensitivity for the detection of unruptured aneurysms was 90.0% with 1.52 FPs per patient. Our computerized scheme can be useful in assisting the radiologists in the detection of unruptured aneurysms in MRA images.

  7. Breast cancer mitosis detection in histopathological images with spatial feature extraction

    NASA Astrophysics Data System (ADS)

    Albayrak, Abdülkadir; Bilgin, Gökhan

    2013-12-01

    In this work, cellular mitosis detection in histopathological images has been investigated. Mitosis detection is very expensive and time consuming process. Development of digital imaging in pathology has enabled reasonable and effective solution to this problem. Segmentation of digital images provides easier analysis of cell structures in histopathological data. To differentiate normal and mitotic cells in histopathological images, feature extraction step is very crucial step for the system accuracy. A mitotic cell has more distinctive textural dissimilarities than the other normal cells. Hence, it is important to incorporate spatial information in feature extraction or in post-processing steps. As a main part of this study, Haralick texture descriptor has been proposed with different spatial window sizes in RGB and La*b* color spaces. So, spatial dependencies of normal and mitotic cellular pixels can be evaluated within different pixel neighborhoods. Extracted features are compared with various sample sizes by Support Vector Machines using k-fold cross validation method. According to the represented results, it has been shown that separation accuracy on mitotic and non-mitotic cellular pixels gets better with the increasing size of spatial window.

  8. A novel scheme for automatic nonrigid image registration using deformation invariant feature and geometric constraint

    NASA Astrophysics Data System (ADS)

    Deng, Zhipeng; Lei, Lin; Zhou, Shilin

    2015-10-01

    Automatic image registration is a vital yet challenging task, particularly for non-rigid deformation images which are more complicated and common in remote sensing images, such as distorted UAV (unmanned aerial vehicle) images or scanning imaging images caused by flutter. Traditional non-rigid image registration methods are based on the correctly matched corresponding landmarks, which usually needs artificial markers. It is a rather challenging task to locate the accurate position of the points and get accurate homonymy point sets. In this paper, we proposed an automatic non-rigid image registration algorithm which mainly consists of three steps: To begin with, we introduce an automatic feature point extraction method based on non-linear scale space and uniform distribution strategy to extract the points which are uniform distributed along the edge of the image. Next, we propose a hybrid point matching algorithm using DaLI (Deformation and Light Invariant) descriptor and local affine invariant geometric constraint based on triangulation which is constructed by K-nearest neighbor algorithm. Based on the accurate homonymy point sets, the two images are registrated by the model of TPS (Thin Plate Spline). Our method is demonstrated by three deliberately designed experiments. The first two experiments are designed to evaluate the distribution of point set and the correctly matching rate on synthetic data and real data respectively. The last experiment is designed on the non-rigid deformation remote sensing images and the three experimental results demonstrate the accuracy, robustness, and efficiency of the proposed algorithm compared with other traditional methods.

  9. Joints and their relations as critical features in action discrimination: evidence from a classification image method.

    PubMed

    van Boxtel, Jeroen J A; Lu, Hongjing

    2015-01-20

    Classifying an action as a runner or a walker is a seemingly effortless process. However, it is difficult to determine which features are used with hypothesis-driven research, because biological motion stimuli generally consist of about a dozen joints, yielding an enormous number of potential relationships among them. Here, we develop a hypothesis-free approach based on a classification image method, using experimental data from relatively few trials (∼1,000 trials per subject). Employing ambiguous actions morphed between a walker and a runner, we identified three types of features that play important roles in discriminating bipedal locomotion presented in a side view: (a) critical joint feature, supported by the finding that the similarity of the movements of feet and wrists to prototypical movements of these joints were most reliably used across all participants; (b) structural features, indicated by contributions from almost all other joints, potentially through a form-based analysis; and (c) relational features, revealed by statistical correlations between joint contributions, specifically relations between the two feet, and relations between the wrists/elbow and the hips. When the actions were inverted, only critical joint features remained to significantly influence discrimination responses. When actions were presented with continuous depth rotation, critical joint features and relational features associated strongly with responses. Using a double-pass paradigm, we estimated that the internal noise is about twice as large as the external noise, consistent with previous findings. Overall, our novel design revealed a rich set of critical features that are used in action discrimination. The visual system flexibly selects a subset of features depending on viewing conditions.

  10. A Statistical-Textural-Features Based Approach for Classification of Solid Drugs Using Surface Microscopic Images

    PubMed Central

    2014-01-01

    The quality of pharmaceutical products plays an important role in pharmaceutical industry as well as in our lives. Usage of defective tablets can be harmful for patients. In this research we proposed a nondestructive method to identify defective and nondefective tablets using their surface morphology. Three different environmental factors temperature, humidity and moisture are analyzed to evaluate the performance of the proposed method. Multiple textural features are extracted from the surface of the defective and nondefective tablets. These textural features are gray level cooccurrence matrix, run length matrix, histogram, autoregressive model and HAAR wavelet. Total textural features extracted from images are 281. We performed an analysis on all those 281, top 15, and top 2 features. Top 15 features are extracted using three different feature reduction techniques: chi-square, gain ratio and relief-F. In this research we have used three different classifiers: support vector machine, K-nearest neighbors and naïve Bayes to calculate the accuracies against proposed method using two experiments, that is, leave-one-out cross-validation technique and train test models. We tested each classifier against all selected features and then performed the comparison of their results. The experimental work resulted in that in most of the cases SVM performed better than the other two classifiers. PMID:25371702

  11. Nonparametric feature extraction for classification of hyperspectral images with limited training samples

    NASA Astrophysics Data System (ADS)

    Kianisarkaleh, Azadeh; Ghassemian, Hassan

    2016-09-01

    Feature extraction plays a crucial role in improvement of hyperspectral images classification. Nonparametric feature extraction methods show better performance compared to parametric ones when distribution of classes is non normal-like. Moreover, they can extract more features than parametric methods do. In this paper, a new nonparametric linear feature extraction method is introduced for classification of hyperspectral images. The proposed method has no free parameter and its novelty can be discussed in two parts. First, neighbor samples are specified by using Parzen window idea for determining local mean. Second, two new weighting functions are used. Samples close to class boundaries will have more weight in the between-class scatter matrix formation and samples close to class mean will have more weight in the within-class scatter matrix formation. The experimental results on three real hyperspectral data sets, Indian Pines, Salinas and Pavia University, demonstrate that the proposed method has better performance in comparison with some other nonparametric and parametric feature extraction methods.

  12. Contourlet Textual Features: Improving the Diagnosis of Solitary Pulmonary Nodules in Two Dimensional CT Images

    PubMed Central

    Wang, Jingjing; Sun, Tao; Gao, Ni; Menon, Desmond Dev; Luo, Yanxia; Gao, Qi; Li, Xia; Wang, Wei; Zhu, Huiping; Lv, Pingxin; Liang, Zhigang; Tao, Lixin; Liu, Xiangtong; Guo, Xiuhua

    2014-01-01

    Objective To determine the value of contourlet textural features obtained from solitary pulmonary nodules in two dimensional CT images used in diagnoses of lung cancer. Materials and Methods A total of 6,299 CT images were acquired from 336 patients, with 1,454 benign pulmonary nodule images from 84 patients (50 male, 34 female) and 4,845 malignant from 252 patients (150 male, 102 female). Further to this, nineteen patient information categories, which included seven demographic parameters and twelve morphological features, were also collected. A contourlet was used to extract fourteen types of textural features. These were then used to establish three support vector machine models. One comprised a database constructed of nineteen collected patient information categories, another included contourlet textural features and the third one contained both sets of information. Ten-fold cross-validation was used to evaluate the diagnosis results for the three databases, with sensitivity, specificity, accuracy, the area under the curve (AUC), precision, Youden index, and F-measure were used as the assessment criteria. In addition, the synthetic minority over-sampling technique (SMOTE) was used to preprocess the unbalanced data. Results Using a database containing textural features and patient information, sensitivity, specificity, accuracy, AUC, precision, Youden index, and F-measure were: 0.95, 0.71, 0.89, 0.89, 0.92, 0.66, and 0.93 respectively. These results were higher than results derived using the database without textural features (0.82, 0.47, 0.74, 0.67, 0.84, 0.29, and 0.83 respectively) as well as the database comprising only textural features (0.81, 0.64, 0.67, 0.72, 0.88, 0.44, and 0.85 respectively). Using the SMOTE as a pre-processing procedure, new balanced database generated, including observations of 5,816 benign ROIs and 5,815 malignant ROIs, and accuracy was 0.93. Conclusion Our results indicate that the combined contourlet textural features of solitary

  13. Geomorphic domains and linear features on Landsat images, Circle Quadrangle, Alaska

    USGS Publications Warehouse

    Simpson, S.L.

    1984-01-01

    A remote sensing study using Landsat images was undertaken as part of the Alaska Mineral Resource Assessment Program (AMRAP). Geomorphic domains A and B, identified on enhanced Landsat images, divide Circle quadrangle south of Tintina fault zone into two regional areas having major differences in surface characteristics. Domain A is a roughly rectangular, northeast-trending area of relatively low relief and simple, widely spaced drainages, except where igneous rocks are exposed. In contrast, domain B, which bounds two sides of domain A, is more intricately dissected showing abrupt changes in slope and relatively high relief. The northwestern part of geomorphic domain A includes a previously mapped tectonostratigraphic terrane. The southeastern boundary of domain A occurs entirely within the adjoining tectonostratigraphic terrane. The sharp geomorphic contrast along the southeastern boundary of domain A and the existence of known faults along this boundary suggest that the southeastern part of domain A may be a subdivision of the adjoining terrane. Detailed field studies would be necessary to determine the characteristics of the subdivision. Domain B appears to be divisible into large areas of different geomorphic terrains by east-northeast-trending curvilinear lines drawn on Landsat images. Segments of two of these lines correlate with parts of boundaries of mapped tectonostratigraphic terranes. On Landsat images prominent north-trending lineaments together with the curvilinear lines form a large-scale regional pattern that is transected by mapped north-northeast-trending high-angle faults. The lineaments indicate possible lithlogic variations and/or structural boundaries. A statistical strike-frequency analysis of the linear features data for Circle quadrangle shows that northeast-trending linear features predominate throughout, and that most northwest-trending linear features are found south of Tintina fault zone. A major trend interval of N.64-72E. in the linear

  14. [Progress on the cause and mechanism of a separation of clinical symptoms and signs and imaging features in lumbar disk herniation].

    PubMed

    Hu, Xing-xin; Liu, Li-min

    2015-10-01

    A few of patients with lumbar disk herniation having a separation of clinical symptoms and signs and imaging features, can be found in clinic, but the traditional theory of direct mechanical compression of nerve roots by herniated nucleus pulposus can't be used to explain this abnormal protrusion of lumbar intervertebral disc. The clinical symptoms and signs of the atypical lumbar disk herniation are affected by multiple factors. The indirect mechanical compression and distraction effect of spinal nerve roots may play an important role in the occurrence of the separation, and the appearance of abnormal clinical symptoms and signs is closely related to the migration of herniated nucleus pulposus tissue, transmission of injury information in the nervous system, and the complex interactions among the nucleus pulposus, dural sac and nerve roots. Moreover,the changes of microcirculation and inflammation secondary to the herniated nucleus pulposus tissue, the hyperosteogeny in the corresponding segment of the lumbar vertebrae and the posture changes all results in a diversity of symptoms and signs in patients with lumbar intervertebral disc herniation. Besides, there exist congenital variation of lumbosacral nerve roots and vertebral bodies in some patients, and the misdiagnosis or missed diagnosis of imaging finding may occur in some cases. However, the appearance of a separation of clinical symptoms and signs and imaging examination in patients may be caused by a variety of reasons in clinic. The exact mechanism involved in the interaction among nucleus pulposus tissue, dural sac and nerve root, secondary changes of pathophysiology and biomechanics around the nucleus pulposus, the determination of lesioned responsible segments, and how to overcome the limitations of imaging all need the further researches. PMID:26727796

  15. Imaging features of primary anorectal gastrointestinal stromal tumors with clinical and pathologic correlation

    PubMed Central

    Koch, M.R.; Jagannathan, J.P.; Krajewski, K.M.; Raut, C.P.; Hornick, J.L.; Ramaiya, N.H.

    2012-01-01

    Abstract Purpose: To evaluate the imaging features of anorectal gastrointestinal stromal tumors (GISTs) with clinical and histopathologic correlation. Materials and methods: In this Institutional Review Board-approved, Health Insurance Portability and Accountability Act-compliant retrospective study, 16 patients (12 men; mean age 66 years (30–89 years)) with pathologically proven anorectal GISTs seen at our institution from January 2001 to July 2011 were identified. Electronic medical records were reviewed to obtain clinical data. Pretreatment imaging studies (computed tomography (CT) in 16 patients, magnetic resonance imaging (MRI) in 9 patients and fluorodeoxyglucose (FDG)-positron emission tomography (PET)/CT in 8 patients) were evaluated by 2 radiologists until consensus. The location, size and imaging features of the primary tumor and metastases at presentation, if any, were recorded, and correlated with clinical data and pathologic features (histologic type, presence of necrosis, mitotic activity, risk category, immunohistochemical profile). Results: The mean tumor size was 6.9 × 6.0 cm. Of the 16 tumors, 11 (68.7%) were infralevator, 4 (25%) supra and infralevator and 1 (6.3%) supralevator; 9 (56.2%) were exophytic, 6 (37.5%) both exophytic and intraluminal, and 1 (6.3%) was intraluminal. The tumors were iso- to minimally hypoattenuating to muscle on CT, iso- to minimally hypointense on T1-weighted images, hyperintense on T2-weighted images and showed variable enhancement. Necrosis was seen in 4 (25%), and hemorrhage and calcification in 2 (12.5%) patients each. The tumors were FDG avid with a mean maximum standardized uptake value of 11 (8.4–16.8). All tumors were positive for KIT and CD34. Distant metastasis to liver was seen in 1 patient (6.3%) at presentation. Conclusion: Anorectal GISTs are well-circumscribed, non-circumferential, predominantly infralevator, intramural or exophytic, FDG-avid, hypoattenuating masses, and present without

  16. Land Cover Change Detection Based on Genetically Feature Aelection and Image Algebra Using Hyperion Hyperspectral Imagery

    NASA Astrophysics Data System (ADS)

    Seydi, S. T.; Hasanlou, M.

    2015-12-01

    The Earth has always been under the influence of population growth and human activities. This process causes the changes in land use. Thus, for optimal management of the use of resources, it is necessary to be aware of these changes. Satellite remote sensing has several advantages for monitoring land use/cover resources, especially for large geographic areas. Change detection and attribution of cultivation area over time present additional challenges for correctly analyzing remote sensing imagery. In this regards, for better identifying change in multi temporal images we use hyperspectral images. Hyperspectral images due to high spectral resolution created special placed in many of field. Nevertheless, selecting suitable and adequate features/bands from this data is crucial for any analysis and especially for the change detection algorithms. This research aims to automatically feature selection for detect land use changes are introduced. In this study, the optimal band images using hyperspectral sensor using Hyperion hyperspectral images by using genetic algorithms and Ratio bands, we select the optimal band. In addition, the results reveal the superiority of the implemented method to extract change map with overall accuracy by a margin of nearly 79% using multi temporal hyperspectral imagery.

  17. A comparison of feature extraction methods for Sentinel-1 images: Gabor and Weber transforms

    NASA Astrophysics Data System (ADS)

    Stan, Mihaela; Popescu, Anca; Stoichescu, Dan Alexandru

    2015-10-01

    The purpose of this paper is to compare the performance of two feature extraction methods when applied on high resolution Synthetic Aperture Radar (SAR) images acquired with the new ESA mission SENTINEL-1 (S-1). The feature extraction methods were previously tested on high and very high resolution SAR data (imaged by TerraSAR-X) and had a good performance in discriminating between a relevant numbers of land cover classes (tens of classes). Based on the available spatial resolution (10x10m) of S-1 Interferometric Wide (IW) Ground Range Detected (GRD) images the number of detectable classes is much lower. Moreover, the overall heterogeneity of the images is much lower as compared to the high resolution data, the number of observable details is smaller, and this favors the choice of a smaller window size for the analysis: between 10 and 50 pixels in range and azimuth. The size of the analysis window ensures the consistency with the previous results reported in the literature in very high resolution data (as the size on the ground is comparable and thus the number of contributing objects in the window is similar). The performance of Gabor filters and the Weber Local Descriptor (WLD) was investigated in a twofold approach: first the descriptors were computed directly over the IW GRD images and secondly on the sub-sampled version of the same data (in order to determine the effect of the speckle correlation on the overall class detection probability).

  18. Feature evaluation of complex hysteresis smoothing and its practical applications to noisy SEM images.

    PubMed

    Suzuki, Kazuhiko; Oho, Eisaku

    2013-01-01

    Quality of a scanning electron microscopy (SEM) image is strongly influenced by noise. This is a fundamental drawback of the SEM instrument. Complex hysteresis smoothing (CHS) has been previously developed for noise removal of SEM images. This noise removal is performed by monitoring and processing properly the amplitude of the SEM signal. As it stands now, CHS may not be so utilized, though it has several advantages for SEM. For example, the resolution of image processed by CHS is basically equal to that of the original image. In order to find wide application of the CHS method in microscopy, the feature of CHS, which has not been so clarified until now is evaluated correctly. As the application of the result obtained by the feature evaluation, cursor width (CW), which is the sole processing parameter of CHS, is determined more properly using standard deviation of noise Nσ. In addition, disadvantage that CHS cannot remove the noise with excessively large amplitude is improved by a certain postprocessing. CHS is successfully applicable to SEM images with various noise amplitudes.

  19. Interactive Feature Space Explorer© for Multi–Modal Magnetic Resonance Imaging

    PubMed Central

    Türkbey, Barış; Choyke, Peter L.; Akin, Oguz; Aras, Ömer; Mun, Seong K.

    2015-01-01

    Wider information content of multi–modal biomedical imaging is advantageous for detection, diagnosis and prognosis of various pathologies. However, the necessity to evaluate a large number images might hinder these advantages and reduce the efficiency. Herein, a new computer aided approach based on the utilization of feature space (FS) with reduced reliance on multiple image evaluations is proposed for research and routine clinical use. The method introduces the physician experience into the discovery process of FS biomarkers for addressing biological complexity, e.g., disease heterogeneity. This, in turn, elucidates relevant biophysical information which would not be available when automated algorithms are utilized. Accordingly, the prototype platform was designed and built for interactively investigating the features and their corresponding anatomic loci in order to identify pathologic FS regions. While the platform might be potentially beneficial in decision support generally and specifically for evaluating outlier cases, it is also potentially suitable for accurate ground truth determination in FS for algorithm development. Initial assessments conducted on two different pathologies from two different institutions provided valuable biophysical perspective. Investigations of the prostate magnetic resonance imaging data resulted in locating a potential aggressiveness biomarker in prostate cancer. Preliminary findings on renal cell carcinoma imaging data demonstrated potential for characterization of disease subtypes in the FS. PMID:25868623

  20. De-Striping for Tdiccd Remote Sensing Image Based on Statistical Features of Histogram

    NASA Astrophysics Data System (ADS)

    Gao, Hui-ting; Liu, Wei; He, Hong-yan; Zhang, Bing-xian; Jiang, Cheng

    2016-06-01

    Aim to striping noise brought by non-uniform response of remote sensing TDI CCD, a novel de-striping method based on statistical features of image histogram is put forward. By analysing the distribution of histograms,the centroid of histogram is selected to be an eigenvalue representing uniformity of ground objects,histogrammic centroid of whole image and each pixels are calculated first,the differences between them are regard as rough correction coefficients, then in order to avoid the sensitivity caused by single parameter and considering the strong continuity and pertinence of ground objects between two adjacent pixels,correlation coefficient of the histograms is introduces to reflect the similarities between them,fine correction coefficient is obtained by searching around the rough correction coefficient,additionally,in view of the influence of bright cloud on histogram,an automatic cloud detection based on multi-feature including grey level,texture,fractal dimension and edge is used to pre-process image.Two 0-level panchromatic images of SJ-9A satellite with obvious strip noise are processed by proposed method to evaluate the performance, results show that the visual quality of images are improved because the strip noise is entirely removed,we quantitatively analyse the result by calculating the non-uniformity ,which has reached about 1% and is better than histogram matching method.

  1. Hematopoietic tumors of the female genital system: imaging features with pathologic correlation.

    PubMed

    Salem, Usama; Menias, Christine O; Shaaban, Akram; Bhosale, Priya R; Youssef, Ayda; Elsayes, Khaled M

    2014-08-01

    Various hematopoietic neoplasms can involve the female genital system. The most common hematological malignancy that involves the female genital system is lymphoma and secondary involvement is more common than primary genital lymphoma. Rarely, leukemic infiltration and extramedullary plasmacytomas of the female genital tract may also occur. Being infrequent, these lesions are commonly misdiagnosed radiologically. Therefore, understanding these malignancies of the female genital system and recognizing their imaging features are of utmost clinical importance. Although definitive diagnosis can be made only by histological analysis, imaging of these tumors plays an important role in detecting lesion extensions, guiding biopsies, staging disease, planning therapy, and detecting recurrence.

  2. The analysis of solar activity features by means of the BASIS digital imaging system

    NASA Astrophysics Data System (ADS)

    Messerotti, M.; Lampi, L.; Furlani, S.; Zlobec, P.

    CCD-based digital imaging systems are powerful tools for the analysis of solar activity features in real-time or as post-processing. Despite the actual sensor-limited resolutions of low-cost systems, interesting projects can be carried out such as, for instance, the tracing of photospheric motions, in principle also in automatic mode. With regard to that BASIS, a digital imaging system for the sun operated at Trieste, will be briefly described. Possible applications as mentioned above will be discussed with emphasis on photospheric and chromospheric patterns.

  3. Experimenting Liver Fibrosis Diagnostic by Two Photon Excitation Microscopy and Bag-of-Features Image Classification

    NASA Astrophysics Data System (ADS)

    Stanciu, Stefan G.; Xu, Shuoyu; Peng, Qiwen; Yan, Jie; Stanciu, George A.; Welsch, Roy E.; So, Peter T. C.; Csucs, Gabor; Yu, Hanry

    2014-04-01

    The accurate staging of liver fibrosis is of paramount importance to determine the state of disease progression, therapy responses, and to optimize disease treatment strategies. Non-linear optical microscopy techniques such as two-photon excitation fluorescence (TPEF) and second harmonic generation (SHG) can image the endogenous signals of tissue structures and can be used for fibrosis assessment on non-stained tissue samples. While image analysis of collagen in SHG images was consistently addressed until now, cellular and tissue information included in TPEF images, such as inflammatory and hepatic cell damage, equally important as collagen deposition imaged by SHG, remain poorly exploited to date. We address this situation by experimenting liver fibrosis quantification and scoring using a combined approach based on TPEF liver surface imaging on a Thioacetamide-induced rat model and a gradient based Bag-of-Features (BoF) image classification strategy. We report the assessed performance results and discuss the influence of specific BoF parameters to the performance of the fibrosis scoring framework.

  4. Feature-enhanced synthetic aperture radar image formation based on nonquadratic regularization.

    PubMed

    Cetin, M; Karl, W C

    2001-01-01

    We develop a method for the formation of spotlight-mode synthetic aperture radar (SAR) images with enhanced features. The approach is based on a regularized reconstruction of the scattering field which combines a tomographic model of the SAR observation process with prior information regarding the nature of the features of interest. Compared to conventional SAR techniques, the method we propose produces images with increased resolution, reduced sidelobes, reduced speckle and easier-to-segment regions. Our technique effectively deals with the complex-valued, random-phase nature of the underlying SAR reflectivities. An efficient and robust numerical solution is achieved through extensions of half-quadratic regularization methods to the complex-valued SAR problem. We demonstrate the performance of the method on synthetic and real SAR scenes.

  5. Simulation of target interpretation based on infrared image features and psychology principle

    NASA Astrophysics Data System (ADS)

    Lin, Wei; Chen, Yu-hua; Gao, Hong-sheng; Wang, Zhan-feng; Wang, Ji-jun; Su, Rong-hua; Huang, Yan-ping

    2009-07-01

    It's an important and complicated process in target interpretation that target features extraction and identification, which effect psychosensorial quantity of interpretation person to target infrared image directly, and decide target viability finally. Using statistical decision theory and psychology principle, designing four psychophysical experiment, the interpretation model of the infrared target is established. The model can get target detection probability by calculating four features similarity degree between target region and background region, which were plotted out on the infrared image. With the verification of a great deal target interpretation in practice, the model can simulate target interpretation and detection process effectively, get the result of target interpretation impersonality, which can provide technique support for target extraction, identification and decision-making.

  6. Feature Selection and Classification of Hyperspectral Images with Support Vector Machines

    SciTech Connect

    Archibald, Richard K; Fann, George I

    2007-01-01

    Hyperspectral images consist of large number of bands which require sophisticated analysis to extract. One approach to reduce computational cost, information representation, and accelerate knowledge discovery is to eliminate bands that do not add value to the classification and analysis method which is being applied. In particular, algorithms that perform band elimination should be designed to take advantage of the structure of the classification method used. This letter introduces an embedded-feature-selection (EFS) algorithm that is tailored to operate with support vector machines (SVMs) to perform band selection and classification simultaneously. We have successfully applied this algorithm to determine a reasonable subset of bands without any user-defined stopping criteria on some sample AVIRIS images; a problem occurs in benchmarking recursive-feature-elimination methods for the SVMs.

  7. Long Term Variations in Chromospheric Features from Ca-K Images at Kodaikanal

    NASA Astrophysics Data System (ADS)

    Priyal, Muthu; Singh, Jagdev; Ravindra, B.; Priya, T. G.; Amareswari, K.

    2014-01-01

    There is a collection of about 100 years of Ca-K line spectroheliograms at the Kodaikanal Observatory (KKL) obtained on daily basis with a single instrument that can be used to study long term variations of various chromospheric features. All the Ca-K images have been digitized using specially developed digitizers with uniform and highly stable light source, high quality lens and 4k×4k format CCD camera. The digitization has been carried out in a room with controlled temperature and humidity. The digitized data are in 16-bit format with pixel resolution of 0.86 arcsec. The digitized images have been calibrated by a process that includes flat-fielding, density to intensity conversion, centering the image, and rotation of the image to make the solar north pole in the fixed direction. Then we applied correction for the limb darkening effect and also made the background in the image uniform. The image background was normalized to unity that enabled us to use the intensity contrast to identify different features, such as plages, enhanced (EN), active (AN), and quite network on images and classified them by using different image contrast and area threshold values. After several experiments with different threshold values for different features and careful analysis of a large number of images, we could fix the threshold values of intensity contrast larger than 1.35 and area larger than 1 arcmin2 for plages, larger than 1.35 but area less than 1 arcmin2 for EN, and between 1.25 - 1.35 for AN. We compared the quarterly averaged and half yearly averaged plage areas obtained from KKL with the Mount Wilson (MWO) data and sunspot number. We find that the plage area extracted from the KKL is highly correlated with the MWO plage area, though there is a slight difference between the two data set in cycle 19. The plage area of KKL is also highly correlated with the sunspot number. The areas of EN and AN are also found to have smaller quasi-periodic variations apart from the solar

  8. Contrast-enhanced multiple-phase imaging features in hepatic epithelioid hemangioendothelioma

    PubMed Central

    Chen, Ying; Yu, Ri-Sheng; Qiu, Ling-Ling; Jiang, Ding-Yao; Tan, Yan-Bin; Fu, Yan-Biao

    2011-01-01

    AIM: To investigate and review the contrast-enhanced multiple-phase computed tomography (CEMP CT) and magnetic resonance imaging (MRI) findings in patients with pathologically confirmed hepatic epithelioid hemangioendothelioma (HEHE). METHODS: Findings from imaging examinations in 8 patients (5 women and 3 men) with pathologically confirmed HEHE were retrospectively reviewed (CT images obtained from 7 patients and MR images obtained from 6 patients). The age of presentation varied from 27 years to 60 years (average age 39.8 years). RESULTS: There were two types of HEHE: multifocal type (n = 7) and diffuse type (n = 1). In the multifocal-type cases, there were 74 lesions on CT and 28 lesions on MRI with 7 lesions found with diffusion weighted imaging; 18 (24.3%) of 74 lesions on plain CT and 26 (92.9%) of 28 lesions on pre-contrast MRI showed the target sign. On CEMP CT, 28 (37.8%) of 74 lesions appeared with the target sign and a progressive-enhancement rim and 9 (12.2%) of 74 lesions displayed progressive enhancement, maintaining a state of persistent enhancement. On CEMP MRI, 27 (96.4%) of 28 lesions appeared with the target sign with a progressive-enhancement rim and 28 (100%) of 28 lesions displayed progressive-enhancement, maintaining a state of persistent enhancement. In the diffuse-type cases, an enlarged liver was observed with a large nodule appearing with persistent enhancement on CEMP CT and MRI. CONCLUSION: The most important imaging features of HEHE are the target sign and/or progressive enhancement with persistent enhancement on CEMP CT and MRI. MRI is advantageous over CT in displaying these imaging features. PMID:21941423

  9. Searching for a traveling feature in Saturn's rings in Cassini Imaging Science Subsystem data

    NASA Astrophysics Data System (ADS)

    Aye, Klaus-Michael; Rehnberg, Morgan; Brown, Zarah; Esposito, Larry W.

    2016-10-01

    Introduction: Using Cassini UVIS occultation data, a traveling wave feature has been identified in the Saturn rings that is most likely caused by the radial positions swap of the moons Janus and Epimetheus [1]. The hypothesis is that non-linear interferences between the linear density waves when being relocated by the moon swap create a solitary wave that is traveling outward through the rings. The observations in [1] further lead to the derivation of values for the radial travel speeds of the identified traveling features, from 39.6 km/yr for the Janus 5:4 resonance up to 45.8 for the Janus 4:3 resonance.Previous confirmations in ISS data: Work in [1] also identified the feature in Cassini Imaging Science Subsystem (ISS) data that was taken around the time of the UVIS occultations where the phenomenon was first discovered, so far one ISS image for each Janus resonances 2:1, 4:3, 5:4, and 6:5.Search guided by predicted locations: Using the observation-fitted radial velocities from [1], we can extrapolate these to identify Saturn radii at which the traveling feature should be found at later times. Using this and new image analysis and plotting tools available in [2], we have identified a potential candidate feature in an ISS image that was taken 2.5 years after the feature causing moon swap in January 2006. We intend to expand our search by identifying candidate ISS data by a meta-database search constraining the radius at future times corresponding to the predicted future locations of the hypothesized solitary wave and present our findings at this conference.References: [1] Rehnberg, M.E., Esposito, L.W., Brown, Z.L., Albers, N., Sremčević, M., Stewart, G.R., 2016. A Traveling Feature in Saturn's Rings. Icarus, accepted in June 2016. [2] K.-Michael Aye. (2016). pyciss: v0.5.0. Zenodo. 10.5281/zenodo.53092

  10. New features in Saturn's atmosphere revealed by high-resolution thermal infrared images

    NASA Technical Reports Server (NTRS)

    Gezari, D. Y.; Mumma, M. J.; Espenak, F.; Deming, D.; Bjoraker, G.; Woods, L.; Folz, W.

    1989-01-01

    Observations of the stratospheric IR emission structure on Saturn are presented. The high-spatial-resolution global images show a variety of new features, including a narrow equatorial belt of enhanced emission at 7.8 micron, a prominent symmetrical north polar hotspot at all three wavelengths, and a midlatitude structure which is asymmetrically brightened at the east limb. The results confirm the polar brightening and reversal in position predicted by recent models for seasonal thermal variations of Saturn's stratosphere.

  11. An explorative childhood pneumonia analysis based on ultrasonic imaging texture features

    NASA Astrophysics Data System (ADS)

    Zenteno, Omar; Diaz, Kristians; Lavarello, Roberto; Zimic, Mirko; Correa, Malena; Mayta, Holger; Anticona, Cynthia; Pajuelo, Monica; Oberhelman, Richard; Checkley, William; Gilman, Robert H.; Figueroa, Dante; Castañeda, Benjamín.

    2015-12-01

    According to World Health Organization, pneumonia is the respiratory disease with the highest pediatric mortality rate accounting for 15% of all deaths of children under 5 years old worldwide. The diagnosis of pneumonia is commonly made by clinical criteria with support from ancillary studies and also laboratory findings. Chest imaging is commonly done with chest X-rays and occasionally with a chest CT scan. Lung ultrasound is a promising alternative for chest imaging; however, interpretation is subjective and requires adequate training. In the present work, a two-class classification algorithm based on four Gray-level co-occurrence matrix texture features (i.e., Contrast, Correlation, Energy and Homogeneity) extracted from lung ultrasound images from children aged between six months and five years is presented. Ultrasound data was collected using a L14-5/38 linear transducer. The data consisted of 22 positive- and 68 negative-diagnosed B-mode cine-loops selected by a medical expert and captured in the facilities of the Instituto Nacional de Salud del Niño (Lima, Peru), for a total number of 90 videos obtained from twelve children diagnosed with pneumonia. The classification capacity of each feature was explored independently and the optimal threshold was selected by a receiver operator characteristic (ROC) curve analysis. In addition, a principal component analysis was performed to evaluate the combined performance of all the features. Contrast and correlation resulted the two more significant features. The classification performance of these two features by principal components was evaluated. The results revealed 82% sensitivity, 76% specificity, 78% accuracy and 0.85 area under the ROC.

  12. SU-E-QI-20: A Review of Advanced PET and CT Image Features for the Evaluation of Tumor Response

    SciTech Connect

    Lu, W

    2014-06-15

    Purpose: To review the literature in using quantitative PET and CT image features for the evaluation of tumor response. Methods: We reviewed and summarized more than fifty papers that use advanced, quantitative PET/CT image features for the evaluation of tumor response. We also discussed future works on extracting disease-specific features, combining multiple and complementary features in response modeling, delineating tumor in multimodality images, and exploring biological explanations of these advanced features. Results: Advanced PET image features considering spatial information, such as tumor volume, tumor shape, total glycolytic volume, histogram distance, and texture features (characterizing spatial distribution of FDG uptake) have been found more informative than the traditional SUVmax for the prediction of tumor response. Advanced CT features, including volumetric, attenuation, morphologic, structure, and texture descriptors, have also been found advantage over the traditional RECIST and WHO criteria in certain tumor types. Conclusions: Advanced, quantitative FDG PET/CT image features have been shown promising for the evaluation of tumor response. With the emerging multi-modality imaging performed at multiple time points for each patient, it becomes more important to analyze the serial images quantitatively, select and combine both complementary and contradictory information from various sources, for accurate and personalized evaluation of tumor response to therapy.

  13. Magnetic resonance imaging features of intracranial astrocytomas and oligodendrogliomas in dogs.

    PubMed

    Young, Benjamin D; Levine, Jonathan M; Porter, Brian F; Chen-Allen, Annie V; Rossmeisl, John H; Platt, Simon R; Kent, Marc; Fosgate, Geoffrey T; Schatzberg, Scott J

    2011-01-01

    Astrocytomas and oligodendrogliomas represent one third of histologically confirmed canine brain tumors. Our purpose was to describe the magnetic resonance (MR) imaging features of histologically confirmed canine intracranial astrocytomas and oligodendrogliomas and to examine for MR features that differentiate these tumor types. Thirty animals with confirmed astrocytoma (14) or oligodendroglioma (16) were studied. All oligodendrogliomas and 12 astrocytomas were located in the cerebrum or thalamus, with the remainder of astrocytomas in the cerebellum or caudal brainstem. Most (27/30) tumors were associated with both gray and white matter. The signal characteristics of both tumor types were hypointense on T1-weighted images (12 each) and hyperintense on T2-weighted images (11/14 astrocytomas, 12/16 oligodendrogliomas). For astrocytomas and oligodendrogliomas, respectively, common findings were contrast enhancement (10/13, 11/15), ring-like contrast enhancement (6/10, 9/11), cystic regions within the mass (7/14, 12/16), and hemorrhage (4/14, 6/16). Oligodendrogliomas were significantly more likely to contact the brain surface (meninges) than astrocytomas (14/16, 7/14, respectively, P=0.046). Contact with the lateral ventricle was the most common finding, occurring in 13/14 astrocytomas and 14/16 oligodendrogliomas. No MR features were identified that reliably distinguished between these two tumor types. Contrast enhancement was more common in high-grade tumors (III or IV) than low-grade tumors (II, P=0.008). PMID:21388463

  14. Usefulness of wavelet-based features as global descriptors of VHR satellite images

    NASA Astrophysics Data System (ADS)

    Pyka, Krystian; Drzewiecki, Wojciech; Bernat, Katarzyna; Wawrzaszek, Anna; Krupiński, Michal

    2014-10-01

    In this paper we present the results of research carried out to assess the usefulness of wavelet-based measures of image texture for classification of panchromatic VHR satellite image content. The study is based on images obtained from EROS-A satellite. Wavelet-based features are calculated according to two approaches. In first one the wavelet energy is calculated for each components from every level of decomposition using Haar wavelet. In second one the variance and kurtosis are calculated as mean values of detail components with filters belonging to the D, LA, MB groups of various lengths. The results indicate that both approaches are useful and complement one another. Among the most useful wavelet-based features are present not only those calculated with short or long filters, but also with the filters of intermediate length. Usage of filters of different type and length as well as different statistical parameters (variance, kurtosis) calculated as means for each decomposition level improved the discriminative properties of the feature vector consisted initially of wavelet energies of each component.

  15. Advances in feature selection methods for hyperspectral image processing in food industry applications: a review.

    PubMed

    Dai, Qiong; Cheng, Jun-Hu; Sun, Da-Wen; Zeng, Xin-An

    2015-01-01

    There is an increased interest in the applications of hyperspectral imaging (HSI) for assessing food quality, safety, and authenticity. HSI provides abundance of spatial and spectral information from foods by combining both spectroscopy and imaging, resulting in hundreds of contiguous wavebands for each spatial position of food samples, also known as the curse of dimensionality. It is desirable to employ feature selection algorithms for decreasing computation burden and increasing predicting accuracy, which are especially relevant in the development of online applications. Recently, a variety of feature selection algorithms have been proposed that can be categorized into three groups based on the searching strategy namely complete search, heuristic search and random search. This review mainly introduced the fundamental of each algorithm, illustrated its applications in hyperspectral data analysis in the food field, and discussed the advantages and disadvantages of these algorithms. It is hoped that this review should provide a guideline for feature selections and data processing in the future development of hyperspectral imaging technique in foods.

  16. Combination of Stokes polarized light imaging, roughness metrics and morphological features for the detection of melanoma

    NASA Astrophysics Data System (ADS)

    Ghassemi, P.; Shupp, J. W.; Venna, S.; Boisvert, M. E.; Flanagan, K. E.; Jordan, M. H.; Ramella-Roman, J. C.

    2012-02-01

    Skin cancer is the most common and most rapidly increasing form of cancer in the world. Optimal treatment of skin cancer before it reaches metastasis depends critically on early diagnosis. Usually physicians will measure some outward features to diagnose malignancy of pigmented skin lesion. These are mostly morphological features like border irregularity, size, shape, and color. Valuable information can be obtained from the analysis of skin roughness. Previously, we developed a hemispherical imaging Stokes polarimeter to monitor skin cancer based on a roughness assessment of the epidermis. In this study, Stokes images were analyzed to measure polarization properties of skin samples such as the principal angle of the polarization ellipse and the degree of polarization. A processing algorithm based on morphological operators was also developed and applied on Stokes images to extract shape information. Finally, an appropriate classifier was designed to determine the type of lesion based on morphological features as well as the roughness information. Clinical evaluation of the technique was performed on patients with benign nevi, melanocytic nevi, melanoma, and normal skin.

  17. Semisupervised classification for hyperspectral image based on multi-decision labeling and deep feature learning

    NASA Astrophysics Data System (ADS)

    Ma, Xiaorui; Wang, Hongyu; Wang, Jie

    2016-10-01

    Semisupervised learning is widely used in hyperspectral image classification to deal with the limited training samples, however, some more information of hyperspectral image should be further explored. In this paper, a novel semisupervised classification based on multi-decision labeling and deep feature learning is presented to exploit and utilize as much information as possible to realize the classification task. First, the proposed method takes two decisions to pre-label each unlabeled sample: local decision based on weighted neighborhood information is made by the surrounding samples, and global decision based on deep learning is performed by the most similar training samples. Then, some unlabeled ones with high confidence are selected to extent the training set. Finally, self decision, which depends on the self features exploited by deep learning, is employed on the updated training set to extract spectral-spatial features and produce classification map. Experimental results with real data indicate that it is an effective and promising semisupervised classification method for hyperspectral image.

  18. Hierarchical image feature extraction by an irregular pyramid of polygonal partitions

    SciTech Connect

    Skurikhin, Alexei N

    2008-01-01

    We present an algorithmic framework for hierarchical image segmentation and feature extraction. We build a successive fine-to-coarse hierarchy of irregular polygonal partitions of the original image. This multiscale hierarchy forms the basis for object-oriented image analysis. The framework incorporates the Gestalt principles of visual perception, such as proximity and closure, and exploits spectral and textural similarities of polygonal partitions, while iteratively grouping them until dissimilarity criteria are exceeded. Seed polygons are built upon a triangular mesh composed of irregular sized triangles, whose spatial arrangement is adapted to the image content. This is achieved by building the triangular mesh on the top of detected spectral discontinuities (such as edges), which form a network of constraints for the Delaunay triangulation. The image is then represented as a spatial network in the form of a graph with vertices corresponding to the polygonal partitions and edges reflecting their relations. The iterative agglomeration of partitions into object-oriented segments is formulated as Minimum Spanning Tree (MST) construction. An important characteristic of the approach is that the agglomeration of polygonal partitions is constrained by the detected edges; thus the shapes of agglomerated partitions are more likely to correspond to the outlines of real-world objects. The constructed partitions and their spatial relations are characterized using spectral, textural and structural features based on proximity graphs. The framework allows searching for object-oriented features of interest across multiple levels of details of the built hierarchy and can be generalized to the multi-criteria MST to account for multiple criteria important for an application.

  19. [Atypical presentation of preeclampsia].

    PubMed

    Ditisheim, A; Boulvain, M; Irion, O; Pechère-Bertschi, A

    2015-09-01

    Preeclampsia is a pregnancy-related syndrome, which still represents one of the major causes of maternal-fetal mortality and morbidity. Diagnosis can be made difficult due to the complexity of the disorder and its wide spectrum of clinical manifestations. In order to provide an efficient diagnostic tool to the clinician, medical societies regularly rethink the definition criteria. However, there are still clinical presentations of preeclampsia that escape the frame of the definition. The present review will address atypical forms of preeclampsia, such as preeclampsia without proteinuria, normotensive preeclampsia, preeclampsia before 20 weeks of gestation and post-partum preeclampsia.

  20. MDCT of acute colitis in adults: an update in current imaging features.

    PubMed

    Barral, M; Boudiaf, M; Dohan, A; Hoeffel, C; Camus, M; Pautrat, K; Fishman, E K; Cohen, S; Soyer, P

    2015-02-01

    Acute colitis is often diagnosed on multidetector row computed tomography (MDCT) because patients with this condition present with abdominal pain and a variety of nonspecific symptoms. Acute colitis has multiple causes with varying degrees of severity. Analysis of the extent of colonic involvement, presence of specific MDCT imaging features and associated signs should help radiologist narrow the diagnosis. Integrating the results of clinical examination and biological tests is mandatory, and in case of ambiguous or nonspecific MDCT findings, endoscopy and colon biopsy should always be considered for a definite diagnosis. The purpose of this review is to discuss and illustrate MDCT features that are helpful for characterizing acute colitis in adults and to provide an update in current MDCT features. PMID:24835625

  1. MDCT of acute colitis in adults: an update in current imaging features.

    PubMed

    Barral, M; Boudiaf, M; Dohan, A; Hoeffel, C; Camus, M; Pautrat, K; Fishman, E K; Cohen, S; Soyer, P

    2015-02-01

    Acute colitis is often diagnosed on multidetector row computed tomography (MDCT) because patients with this condition present with abdominal pain and a variety of nonspecific symptoms. Acute colitis has multiple causes with varying degrees of severity. Analysis of the extent of colonic involvement, presence of specific MDCT imaging features and associated signs should help radiologist narrow the diagnosis. Integrating the results of clinical examination and biological tests is mandatory, and in case of ambiguous or nonspecific MDCT findings, endoscopy and colon biopsy should always be considered for a definite diagnosis. The purpose of this review is to discuss and illustrate MDCT features that are helpful for characterizing acute colitis in adults and to provide an update in current MDCT features.

  2. Extraction of ABCD rule features from skin lesions images with smartphone.

    PubMed

    Rosado, Luís; Castro, Rui; Ferreira, Liliana; Ferreira, Márcia

    2012-01-01

    One of the greatest challenges in dermatology today is the early detection of melanoma since the success rates of curing this type of cancer are very high if detected during the early stages of its development. The main objective of the work presented in this paper is to create a prototype of a patient-oriented system for skin lesion analysis using a smartphone. This work aims at implementing a self-monitoring system that collects, processes, and stores information of skin lesions through the automatic extraction of specific visual features. The selection of the features was based on the ABCD rule, which considers 4 visual criteria considered highly relevant for the detection of malignant melanoma. The algorithms used to extract these features are briefly described and the results achieved using images taken from the smartphone camera are discussed.

  3. Spatial-temporal features of thermal images for Carpal Tunnel Syndrome detection

    NASA Astrophysics Data System (ADS)

    Estupinan Roldan, Kevin; Ortega Piedrahita, Marco A.; Benitez, Hernan D.

    2014-02-01

    Disorders associated with repeated trauma account for about 60% of all occupational illnesses, Carpal Tunnel Syndrome (CTS) being the most consulted today. Infrared Thermography (IT) has come to play an important role in the field of medicine. IT is non-invasive and detects diseases based on measuring temperature variations. IT represents a possible alternative to prevalent methods for diagnosis of CTS (i.e. nerve conduction studies and electromiography). This work presents a set of spatial-temporal features extracted from thermal images taken in healthy and ill patients. Support Vector Machine (SVM) classifiers test this feature space with Leave One Out (LOO) validation error. The results of the proposed approach show linear separability and lower validation errors when compared to features used in previous works that do not account for temperature spatial variability.

  4. Systematic Literature Review of Imaging Features of Spinal Degeneration in Asymptomatic Populations

    PubMed Central

    Brinjikji, W.; Luetmer, P.H.; Comstock, B.; Bresnahan, B.W.; Chen, L.E.; Deyo, R.A.; Halabi, S.; Turner, J.A.; Avins, A.L.; James, K.; Wald, J.T.; Kallmes, D.F.; Jarvik, J.G.

    2015-01-01

    BACKGROUND AND PURPOSE Degenerative changes are commonly found in spine imaging but often occur in pain-free individuals as well as those with back pain. We sought to estimate the prevalence, by age, of common degenerative spine conditions by performing a systematic review studying the prevalence of spine degeneration on imaging in asymptomatic individuals. MATERIALS AND METHODS We performed a systematic review of articles reporting the prevalence of imaging findings (CT or MR imaging) in asymptomatic individuals from published English literature through April 2014. Two reviewers evaluated each manuscript. We selected age groupings by decade (20, 30, 40, 50, 60, 70, 80 years), determining age-specific prevalence estimates. For each imaging finding, we fit a generalized linear mixed-effects model for the age-specific prevalence estimate clustering in the study, adjusting for the midpoint of the reported age interval. RESULTS Thirty-three articles reporting imaging findings for 3110 asymptomatic individuals met our study inclusion criteria. The prevalence of disk degeneration in asymptomatic individuals increased from 37% of 20-year-old individuals to 96% of 80-year-old individuals. Disk bulge prevalence increased from 30% of those 20 years of age to 84% of those 80 years of age. Disk protrusion prevalence increased from 29% of those 20 years of age to 43% of those 80 years of age. The prevalence of annular fissure increased from 19% of those 20 years of age to 29% of those 80 years of age. CONCLUSIONS Imaging findings of spine degeneration are present in high proportions of asymptomatic individuals, increasing with age. Many imaging-based degenerative features are likely part of normal aging and unassociated with pain. These imaging findings must be interpreted in the context of the patient’s clinical condition. PMID:25430861

  5. GENIE: A HYBRID GENETIC ALGORITHM FOR FEATURE CLASSIFICATION IN MULTI-SPECTRAL IMAGES

    SciTech Connect

    S. PERKINS; ET AL

    2000-12-01

    We consider the problem of pixel-by-pixel classification of a multi-spectral image using supervised learning. Conventional supervised classification techniques such as maximum likelihood classification and less conventional ones such as neural networks, typically base such classifications solely on the spectral components of each pixel. It is easy to see why the color of a pixel provides a nice, bounded, fixed dimensional space in which these classifiers work well. It is often the case however, that spectral information alone is not sufficient to correctly classify a pixel. Maybe spatial neighborhood information is required as well. Or may be the raw spectral components do not themselves make for easy classification, but some arithmetic combination of them would. In either of these cases we have the problem of selecting suitable spatial, spectral or spatio-spectral features that allow the classifier to do its job well. The number of all possible such features is extremely large. How can we select a suitable subset? We have developed GENIE, a hybrid learning system that combines a genetic algorithm that searches a space of image processing operations for a set that can produce suitable feature planes, and a more conventional classifier which uses those feature planes to output a final classification. In this paper we show that the use of a hybrid GA provides significant advantages over using either a GA alone or more conventional classification methods alone. We present results using high-resolution IKONOS data, looking for regions of burned forest and for roads.

  6. Improved superpixel-based polarimetric synthetic aperture radar image classification integrating color features

    NASA Astrophysics Data System (ADS)

    Xing, Yanxiao; Zhang, Yi; Li, Ning; Wang, Robert; Hu, Guixiang

    2016-04-01

    Various polarimetric features including scattering matrix, covariance matrix, polarimetric decomposition results, and textural or spatial information have already been used for polarimetric synthetic aperture radar (PolSAR) image classification. However, color features are rarely involved. We propose an improved superpixel-based PolSAR image classification integrating color features. First, we extract the color information using polarimetric decomposition. Second, by combining the color and spatial information of pixels, modified simple linear iterative clustering is used to generate small regions called superpixels. Then we apply Wishart distance to the superpixels to classify them into different classes. This method is demonstrated using the L-band Flevoland PolSAR data from AirSAR and Oberpfaffenhofen PolSAR data from ESAR. The results show that this method works well for areas with homogeneous terrains like farms in terms of both classification accuracy and computational efficiency. Furthermore, the success of the proposed method signifies that more color features can be discovered in the future research works.

  7. Periprosthetic Atypical Femoral Fracture-like Fracture after Hip Arthroplasty: A Report of Three Cases.

    PubMed

    Lee, Kyung-Jae; Min, Byung-Woo; Jang, Hyung-Kyu; Ye, Hee-Uk; Lim, Kyung-Hwan

    2015-09-01

    Atypical femoral fractures are stress or insufficient fractures induced by low energy trauma or no trauma and have specific X-ray findings. Although the American Society for Bone and Mineral Research has excluded periprosthetic fractures from the definition of an atypical femoral fracture in 2013, this is still a matter of controversy because some authors report periprosthetic fractures showing specific features of atypical fractures around a well-fixed femoral stem. We report 3 cases of periprosthetic femur fractures that had specific radiographic features of atypical femoral fractures in patients with a history of prolonged bisphosphonate use; we also review relevant literature. PMID:27536624

  8. Uranus' Persistent Patterns and Features from High-SNR Imaging in 2012-2014

    NASA Astrophysics Data System (ADS)

    Fry, Patrick M.; Sromovsky, Lawrence A.; de Pater, Imke; Hammel, Heidi B.; Marcus, Phillip

    2015-11-01

    Since 2012, Uranus has been the subject of an observing campaign utilizing high signal-to-noise imaging techniques at Keck Observatory (Fry et al. 2012, Astron. J. 143, 150-161). High quality observing conditions on four observing runs of consecutive nights allowed longitudinally-complete coverage of the atmosphere over a period of two years (Sromovsky et al. 2015, Icarus 258, 192-223). Global mosaic maps made from images acquired on successive nights in August 2012, November 2012, August 2013, and August 2014, show persistent patterns, and six easily distinguished long-lived cloud features, which we were able to track for long periods that ranged from 5 months to over two years. Two at similar latitudes are associated with dark spots, and move with the atmospheric zonal flow close to the location of their associated dark spot instead of following the flow at the latitude of the bright features. These features retained their morphologies and drift rates in spite of several close interactions. A second pair of features at similar latitudes also survived several close approaches. Several of the long-lived features also exhibited equatorward drifts and latitudinal oscillations. Also persistent are a remarkable near-equatorial wave feature and global zonal band structure. We will present imagery, maps, and analyses of these phenomena.PMF and LAS acknowledge support from NASA Planetary Astronomy Program; PMF and LAS acknowledge funding and technical support from W. M. Keck Observatory. We thank those of Hawaiian ancestry on whose sacred mountain we are privileged to be guests. Without their generous hospitality none of our groundbased observations would have been possible.

  9. Evidence for Broadening Criteria for Atypical Depression Which May Define a Reactive Depressive Disorder.

    PubMed

    Silverstein, Brett; Angst, Jules

    2015-01-01

    Objective. Arguing that additional symptoms should be added to the criteria for atypical depression. Method. Published research articles on atypical depression are reviewed. Results. (1) The original studies upon which the criteria for atypical depression were based cited fatigue, insomnia, pain, and loss of weight as characteristic symptoms. (2) Several studies of DSM depressive criteria found patients with atypical depression to exhibit high levels of insomnia, fatigue, and loss of appetite/weight. (3) Several studies have found atypical depression to be comorbid with headaches, bulimia, and body image issues. (4) Most probands who report atypical depression meet criteria for "somatic depression," defined as depression associated with several of disordered eating, poor body image, headaches, fatigue, and insomnia. The gender difference in prevalence of atypical depression results from its overlap with somatic depression. Somatic depression is associated with psychosocial measures related to gender, linking it with the descriptions of atypical depression as "reactive" appearing in the studies upon which the original criteria for atypical depression were based. Conclusion. Insomnia, disordered eating, poor body image, and aches/pains should be added as criteria for atypical depression matching criteria for somatic depression defining a reactive depressive disorder possibly distinct from endogenous melancholic depression. PMID:26258131

  10. Atorvastatin effect evaluation based on feature combination of three-dimension ultrasound images

    NASA Astrophysics Data System (ADS)

    Luo, Yongkang; Ding, Mingyue

    2016-03-01

    In the past decades, stroke has become the worldwide common cause of death and disability. It is well known that ischemic stroke is mainly caused by carotid atherosclerosis. As an inexpensive, convenient and fast means of detection, ultrasound technology is applied widely in the prevention and treatment of carotid atherosclerosis. Recently, many studies have focused on how to quantitatively evaluate local arterial effects of medicine treatment for carotid diseases. So the evaluation method based on feature combination was proposed to detect potential changes in the carotid arteries after atorvastatin treatment. And the support vector machine (SVM) and 10-fold cross-validation protocol were utilized on a database of 5533 carotid ultrasound images of 38 patients (17 atorvastatin groups and 21 placebo groups) at baseline and after 3 months of the treatment. With combination optimization of many features (including morphological and texture features), the evaluation results of single feature and different combined features were compared. The experimental results showed that the performance of single feature is poor and the best feature combination have good recognition ability, with the accuracy 92.81%, sensitivity 80.95%, specificity 95.52%, positive predictive value 80.47%, negative predictive value 95.65%, Matthew's correlation coefficient 76.27%, and Youden's index 76.48%. And the receiver operating characteristic (ROC) curve was also performed well with 0.9663 of the area under the ROC curve (AUC), which is better than all the features with 0.9423 of the AUC. Thus, it is proved that this novel method can reliably and accurately evaluate the effect of atorvastatin treatment.

  11. Unsupervised segmentation of ultrasound images by fusion of spatio-frequential textural features

    NASA Astrophysics Data System (ADS)

    Benameur, S.; Mignotte, M.; Lavoie, F.

    2011-03-01

    Image segmentation plays an important role in both qualitative and quantitative analysis of medical ultrasound images. However, due to their poor resolution and strong speckle noise, segmenting objects from this imaging modality remains a challenging task and may not be satisfactory with traditional image segmentation methods. To this end, this paper presents a simple, reliable, and conceptually different segmentation technique to locate and extract bone contours from ultrasound images. Instead of considering a new elaborate (texture) segmentation model specifically adapted for the ultrasound images, our technique proposes to fuse (i.e. efficiently combine) several segmentation maps associated with simpler segmentation models in order to get a final reliable and accurate segmentation result. More precisely, our segmentation model aims at fusing several K-means clustering results, each one exploiting, as simple cues, a set of complementary textural features, either spatial or frequential. Eligible models include the gray-level co-occurrence matrix, the re-quantized histogram, the Gabor filter bank, and local DCT coefficients. The experiments reported in this paper demonstrate the efficiency and illustrate all the potential of this segmentation approach.

  12. Benign liver tumors in pediatric patients - Review with emphasis on