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Sample records for atypical imaging features

  1. Atypical magnetic resonance imaging features in subacute sclerosing panencephalitis

    PubMed Central

    Das, Biplab; Goyal, Manoj Kumar; Modi, Manish; Mehta, Sahil; Chakravarthi, Sudheer; Lal, Vivek; Vyas, Sameer

    2016-01-01

    Objectives: Subacute sclerosing panencephalitis (SSPE) is rare chronic, progressive encephalitis that affects primarily children and young adults, caused by a persistent infection with measles virus. No cure for SSPE exists, but the condition can be managed by medication if treatment is started at an early stage. Methods and Results: Heterogeneity of imaging findings in SSPE is not very uncommon. But pial and gyral enhancements are very rarely noticed. Significant asymmetric onset as well as pial-gyral enhancements is not reported. Herein we present a case of 16 years adolescent of SSPE having remarkable asymmetric pial-gyral enhancements, which were misinterpreted as tubercular infection. Conclusion: Early diagnosis and treatment is encouraging in SSPE, although it is not curable with current therapy. Clinico-radiological and electrophysiological correlation is very important in diagnosis of SSPE, more gravely in patients having atypical image findings as in our index case. PMID:27293348

  2. Pilocytic astrocytoma presenting with atypical features on magnetic resonance imaging.

    PubMed

    Nakano, Yoshiteru; Yamamoto, Junkoh; Takahashi, Mayu; Soejima, Yoshiteru; Akiba, Daisuke; Kitagawa, Takehiro; Ueta, Kunihiro; Miyaoka, Ryo; Umemura, Takeru; Nishizawa, Shigeru

    2015-10-01

    Pilocytic astrocytoma, which is classified as a grade I astrocytic tumor by the World Health Organization, is the most common type of glioma in children and young adults. Pilocytic astrocytoma generally appears as a well-circumscribed, contrast-enhancing lesion, frequently with cystic components on magnetic resonance imaging (MRI). However, it has been reported that the MRI appearance of pilocytic astrocytoma may be similar to that of high-grade gliomas in some cases. We here report on 6 cases of pilocytic astrocytoma with atypical MRI findings, including small cyst formation, heterogeneously enhancing tumor nodules, irregularly enhancing tumor nodules, and enhancing tumor nodules with internal hemorrhage. All tumors were successfully resected, and the histological diagnoses were pilocytic astrocytoma. When the tumor is located near a cerebral cistern or ventricle, the risk of leptomeningeal dissemination is increased. Furthermore, partial resection has also been associated with a higher risk of recurrence and leptomeningeal dissemination. To date, all but one patient are alive and recurrence-free. Because the preoperative diagnosis influences the decision on the extent of resection and because of the high risk of leptomeningeal dissemination associated with these tumors, careful and correct diagnosis by MRI is important. PMID:25454397

  3. 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

  4. Atypical teratoid/rhabdoid tumors in adult patients: CT and MR imaging features.

    PubMed

    Han, L; Qiu, Y; Xie, C; Zhang, J; Lv, X; Xiong, W; Wang, W; Zhang, X; Wu, P

    2011-01-01

    Primary AT/RT is a rare highly malignant tumor of the CNS, usually occurring in children younger than 5 years of age. The objective of this study was to characterize the CT and MR imaging findings in a series of 5 adult patients with pathologically proved AT/RT. All 5 AT/RTs were supratentorial. In 2 patients who underwent nonenhanced CT, the tumors appeared isoattenuated, and 1 of the 2 tumors contained calcifications. Solid portions of the tumors on MR imaging were isointense on T1-weighted, T2-weighted, and FLAIR images, and 1 case showed restricted diffusion on DWI. The tumors also demonstrated a bandlike rim of strong enhancement surrounding a central cystic area on contrast-enhanced T1-weighted imaging. One tumor was associated with destruction of the calvaria. Although AT/RTs can have nonspecific findings, the tumors in our series were large and isointense on T1-weighted, T2-weighted, and FLAIR images with central necrosis and prominent rim enhancement. PMID:21051520

  5. Uremic parkinsonism with atypical phenotypes and radiologic features.

    PubMed

    Yoon, Jee-Eun; Kim, Ji Sun; Park, Jeong-Ho; Lee, Kyung-Bok; Roh, Hakjae; Park, Sung Tae; Cho, Jin Whan; Ahn, Moo-Young

    2016-04-01

    Uremic encephalopathy with bilateral basal ganglia lesions has been reported as an acute neurometabolic disease which shows reversible clinical course and brain imaging features. The exact nature and pathophysiology have not been well established. We encountered two patients who showed a relapsing and aggravating course and an atypical phenotype including parkinsonism with paroxysmal dystonic head tremor and acute onset monoparesis of the lower extremity. They also showed unusual radiological findings which revealed combined lesions in the basal ganglia and cortex, persistent hemorrhagic transformation, and focal ischemic lesion in the internal capsule. Herein, we present the unusual phenomenology with atypical radiologic findings and suggest the possible multifactorial pathogenesis of uremic encephalopathy. PMID:26631408

  6. Atypical and ischemic features of embolized meningiomas.

    PubMed

    Matsuda, Ken; Takeuchi, Hiroaki; Arai, Yoshikazu; Kitai, Ryuhei; Hosoda, Tetsuya; Tsunetoshi, Kenzo; Arishima, Hidetaka; Sato, Kazufumi; Kikuta, Ken-Ichiro

    2012-01-01

    Preoperative embolization (POE) of meningiomas is widely used to facilitate surgical removal and to reduce intraoperative blood loss. The resulting necrosis and enhanced proliferation have been reported to affect subsequent histologic grading. However, there was little concern about ischemic features, for example small cells resembling atypical meningiomas, cytoplasmic vacuoles resembling clear cell meningioma, intercellular discohesion resembling rhabdoid meningioma, and perivascular cuffs resembling papillary meningioma. Therefore, the extent of these ischemic features was scored and Ki-67 staining indices were investigated in a POE group composed of 29 specimens of meningiomas treated with POE and compared with equivalent results for a non-POE group composed of 29 meningiomas that were not treated with POE. Small cells with high N/C ratios, cytoplasmic vacuoles, intercellular discohesion, and perivascular cuffs were significantly increased in the POE group (versus the non-POE group, p < 0.05). There were no significant differences of the Ki-67 index between the POE group (2.2%) and the non-POE group (1.9%) (p = 0.49). Our results suggest that small cell change resulting in necrosis may be followed by POE, and that clear cell-like, rhabdoid cell-like, or pseudopapillary pattern identified in meningiomas may also be induced by POE. Therefore, histological findings and determination of grading should be evaluated cautiously in cases of embolized meningiomas. PMID:21789536

  7. Depression With Atypical Features: Diagnostic Validity, Prevalence, and Treatment.

    PubMed

    Quitkin, Frederic M.

    2002-06-01

    Depression with atypical features is a treatable and relatively common disorder among depressed outpatients. A growing body of evidence suggests this is a biologically distinct subtype of depression. This assertion is supported by genetic epidemiologic studies and by a preferential response of the subtype to monoamine oxidase inhibitors compared with tricyclic antidepressants. The Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) includes atypical features as a parenthetical modifier for depressive illness. According to DSM-IV diagnostic criteria ("atypical features" specifier), the disorder is primarily characterized by 2 or more of the following symptoms as predominant features in patients with major depression or dysthymic disorder: overeating, oversleeping, "leaden paralysis," and interpersonal rejection sensitivity. Patients also show mood reactivity in response to actual or potential positive events. Despite aspects of the disorder resembling a maladaptive, persistent mode of behavior, patients diagnosed with depression with atypical features demonstrate a good response to antidepressant treatment. PMID:15014736

  8. Imaging of atypical hemangiomas of the liver with pathologic correlation.

    PubMed

    Vilgrain, V; Boulos, L; Vullierme, M P; Denys, A; Terris, B; Menu, Y

    2000-01-01

    Compared with the imaging features of typical hepatic hemangiomas, the imaging features of atypical hepatic hemangiomas have not been well studied or well described. Knowledge of the entire spectrum of atypical hepatic hemangiomas is important and can help one avoid most diagnostic errors. A frequent type of atypical hepatic hemangioma is a lesion with an echoic border at ultrasonography. Less frequent types are large, heterogeneous hemangiomas; rapidly filling hemangiomas; calcified hemangiomas; hyalinized hemangiomas; cystic or multilocular hemangiomas; hemangiomas with fluid-fluid levels; and pedunculated hemangiomas. Adjacent abnormalities consist of arterial-portal venous shunt, capsular retraction, and surrounding nodular hyperplasia; hemangiomas can also develop in cases of fatty liver infiltration. Associated lesions include multiple hemangiomas, hemangiomatosis, focal nodular hyperplasia, and angiosarcoma. Types of atypical evolution are hemangiomas enlarging over time and hemangiomas appearing during pregnancy. Complications consist of inflammation, Kasabach-Merritt syndrome, intratumoral hemorrhage, hemoperitoneum, volvulus, and compression of adjacent structures. In some cases, such as large heterogeneous hemangiomas, calcified hemangiomas, pedunculated hemangiomas, or hemangiomas developing in diffuse fatty liver, a specific diagnosis can be established with imaging, especially magnetic resonance imaging. However, in other atypical cases, the diagnosis will remain uncertain at imaging, and these cases will require histopathologic examination. PMID:10715338

  9. Treating DSM-IV depression with atypical features.

    PubMed

    Stewart, Jonathan W; Thase, Michael E

    2007-04-01

    Depression with atypical features is characterized by mood reactivity and 2 or more symptoms of vegetative reversal (including overeating, oversleeping, severe fatigue or leaden paralysis, and a history of rejection sensitivity). Another important feature of atypical depression is its preferential response to monoamine oxidase inhibitor (MAOI) treatment, especially phenelzine, relative to tricyclic antidepressants (TCAs). The efficacy of newer agents relative to MAOIs and TCAs is unclear. This presentation reviews currently available treatments for DSM-IV depression with atypical features, focusing specifically on placebo-controlled trials. Although phenelzine shows the most efficacy in this population, treatment with TCAs, selective serotonin reuptake inhibitors, cognitive-behavioral therapy, MAOIs other than phenelzine, and other agents are discussed. Following this presentation is a discussion on the treatment of depression with atypical features by experts in this subject area. PMID:17474800

  10. Acute hemorrhagic leukoencephalitis with atypical features.

    PubMed

    Catalan, Mauro; Naccarato, Marcello; Grandi, Fabio Chiodo; Capozzoli, Francesca; Koscica, Nadia; Pizzolato, Gilberto

    2009-02-01

    Acute hemorrhagic leukoencephalitis (AHL) is a rare demyelinating disease mainly affecting children, characterized by acute onset, progressive course and high mortality. A 62-year-old man was admitted to our Unit for diplopia and ataxia ensuing 2 weeks after the onset of pneumonia. MRI T2-weighted images showed signal hyperintensities in the brainstem. Antibodies against Mycoplasma Pneumoniae and cold agglutinins were found. Two weeks later the patient had a worsening of his conditions: he developed left hemiplegia with motor focal seizures and the day after he was deeply comatose (GCS = 4). A second MRI scan showed extensive hyperintensities involving the whole right hemisphere white matter with a small parietal hemorrhagic area. The clinical and neuroimaging features suggested the diagnosis of AHL, Aciclovir in association with steroid therapy were administered and then plasmapheresis was started. After 30 days of coma, the patient gradually reacquired consciousness and motor functions; anyway a left hemiplegia persisted. PMID:19145402

  11. 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

  12. Transformation of a meningioma with atypical imaging.

    PubMed

    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

  13. 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

  14. Clinical features of Friedreich's ataxia: classical and atypical phenotypes.

    PubMed

    Parkinson, Michael H; Boesch, Sylvia; Nachbauer, Wolfgang; Mariotti, Caterina; Giunti, Paola

    2013-08-01

    One hundred and fifty years since Nikolaus Friedreich's first description of the degenerative ataxic syndrome which bears his name, his description remains at the core of the classical clinical phenotype of gait and limb ataxia, poor balance and coordination, leg weakness, sensory loss, areflexia, impaired walking, dysarthria, dysphagia, eye movement abnormalities, scoliosis, foot deformities, cardiomyopathy and diabetes. Onset is typically around puberty with slow progression and shortened life-span often related to cardiac complications. Inheritance is autosomal recessive with the vast majority of cases showing an unstable intronic GAA expansion in both alleles of the frataxin gene on chromosome 9q13. A small number of cases are caused by a compound heterozygous expansion with a point mutation or deletion. Understanding of the underlying molecular biology has enabled identification of atypical phenotypes with late onset, or atypical features such as retained reflexes. Late-onset cases tend to have slower progression and are associated with smaller GAA expansions. Early-onset cases tend to have more rapid progression and a higher frequency of non-neurological features such as diabetes, cardiomyopathy, scoliosis and pes cavus. Compound heterozygotes, including those with large deletions, often have atypical features. In this paper, we review the classical and atypical clinical phenotypes of Friedreich's ataxia. PMID:23859346

  15. The validity of major depression with atypical features based on a community study.

    PubMed

    Horwath, E; Johnson, J; Weissman, M M; Hornig, C D

    1992-10-01

    This article reports on evidence for the validity of major depression (MDD) with atypical features (defined as overeating and oversleeping) as a distinct subtype based on cross-sectional and 1-year prospective data from the Epidemiologic Catchment Area study. MDD with atypical features, when compared to MDD without atypical features, was associated with a younger age of onset, more psychomotor slowing, and more comorbid panic disorder, drug abuse or dependence, and somatization disorder. These differences could not be explained by differences in demographic characteristics or by symptom severity. This study, based on a community sample, found that major depression with atypical features may constitute a distinct subtype. PMID:1447429

  16. Imaging appearances of atypical hydatid cysts

    PubMed Central

    Malik, Amita; Chandra, Ranjan; Prasad, Rajni; Khanna, Geetika; Thukral, Brij B

    2016-01-01

    Hydatid disease continues to be a significant health problem in many parts of the world. It can occur in any part of the body, but liver is the commonest site of involvement. The disease may remain asymptomatic for years. Symptoms occur due to compression of local structures or complications like rupture and infection. The diagnosis is clear when typical radiological appearance is observed at the common sites of involvement. Complications give rise to atypical appearances. These coupled with unusual localizations pose diagnostic difficulty. The aim of this pictorial essay is to demonstrate the atypical manifestations of hydatid cysts – atypical either due to complications or the unusual site. PMID:27081221

  17. Gangliocytic Paraganglioma With Atypical Immunohistochemical Features Presenting as Extrahepatic Biliary Obstruction.

    PubMed

    Sharma, Saniya; Gaspar, Balan Louis; Kumar, Pradeep; Yadav, Thakur Deen; Vasishta, Rakesh Kumar

    2015-10-01

    Gangliocytic paraganglioma is a rare benign tumor of upper gastrointestinal tract that most commonly involves the second part of duodenum. The tumor is detected incidentally on imaging in most of the cases. However, presentation with extrahepatic biliary obstruction is extremely rare. We recently encountered a 50-year-old male patient who was evaluated for extrahepatic biliary obstruction and was found to have a periampullary mass on imaging. The patient underwent pylorus-preserving pancreaticoduodenectomy along with liver biopsy and hepatoduodenal lymph node dissection. On histopathological examination, a tumor was detected in the periampullary region of duodenum, which was confirmed to be gangliocytic paraganglioma on immunohistochemistry along with atypical histological and immunohistochemical features. PMID:26081293

  18. Atypical radiographic features of skull base cholesterol granuloma.

    PubMed

    Dinh, Christine T; Goncalves, Stefania; Bhatia, Rita; Truong, Kim; Telischi, Fred; Angeli, Simon; Morcos, Jacques; Eshraghi, Adrien A

    2016-06-01

    Cholesterol granulomas (CGs) are the most common benign lesions of the petrous apex (PA) and have distinct computed tomography (CT) and magnetic resonance imaging (MRI) characteristics. On CT, CGs of the PA (PACG) present as expansile lesions with erosion of bony trabeculae. MRI shows a hyperintense lesion on T1-and T2-weighted images and do not enhance with gadolinium. The objective is to describe the radiographic features of CGs of the skull base that do not arise from the PA. This study is a retrospective review. Three patients were operated on for suspected recurrent endolymphatic sac tumor, intracranial cholesteatoma, and recurrent sphenoid wing meningioma based on CT and MRI findings. Pathology results were consistent with CG in all three cases. All patients had bone erosion on CT. These skull base CGs did not demonstrate similar MRI features. These lesions were hyperintense, iso-to-hyperintense, and hypointense on T1-weighted MRI, respectively. These CGs were hyperintense in two cases and iso-to-hyperintense in one case on T2-weighted MRI. These lesions either demonstrated central or rim enhancement after gadolinium administration. Skull base CGs that do not arise from the PA demonstrate a broad spectrum of radiographic characteristics on MRI that are not typical of PACG. PMID:26164292

  19. Multispectral Image Feature Points

    PubMed Central

    Aguilera, Cristhian; Barrera, Fernando; Lumbreras, Felipe; Sappa, Angel D.; Toledo, Ricardo

    2012-01-01

    This paper presents a novel feature point descriptor for the multispectral image case Far-Infrared and Visible Spectrum images. It allows matching interest points on images of the same scene but acquired in different spectral bands. Initially, points of interest are detected on both images through a SIFT-like based scale space representation. Then, these points are characterized using an Edge Oriented Histogram (EOH) descriptor. Finally, points of interest from multispectral images are matched by finding nearest couples using the information from the descriptor. The provided experimental results and comparisons with similar methods show both the validity of the proposed approach as well as the improvements it offers with respect to the current state-of-the-art.

  20. 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

  1. 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

  2. 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

  3. The high prevalence of "soft" bipolar (II) features in atypical depression.

    PubMed

    Perugi, G; Akiskal, H S; Lattanzi, L; Cecconi, D; Mastrocinque, C; Patronelli, A; Vignoli, S; Bemi, E

    1998-01-01

    Seventy-two percent of 86 major depressive patients with atypical features as defined by the DSM-IV and evaluated systematically were found to meet our criteria for bipolar II and related "soft" bipolar disorders; nearly 60% had antecedent cyclothymic or hyperthymic temperaments. The family history for bipolar disorder validated these clinical findings. Even if we limit the diagnosis of bipolar II to the official DSM-IV threshold of 4 days of hypomania, 32.6% of atypical depressives in our sample would meet this conservative threshold, a rate that is three times higher than the estimates of bipolarity among atypical depressives in the literature. By definition, mood reactivity was present in all patients, while interpersonal sensitivity occurred in 94%. Lifetime comorbidity rates were as follows: social phobia 30%, body dysmorphic disorder 42%, obsessive-compulsive disorder 20%, and panic disorder (agoraphobia) 64%. Both cluster A (anxious personality) and cluster B (e.g., borderline and histrionic) personality disorders were highly prevalent. These data suggest that the "atypicality" of depression is favored by affective temperamental dysregulation and anxiety comorbidity, clinically manifesting in a mood disorder subtype that is preponderantly in the realm of bipolar II. In the present sample, only 28% were strictly unipolar and characterized by avoidant and social phobic features, without histrionic traits. PMID:9515190

  4. Primary Follicular Lymphoma of the Duodenum with Erosions as Atypical Macroscopic Features

    PubMed Central

    Takeuchi, Keiko; Iwamuro, Masaya; Imagawa, Atsushi; Kubota, Yoshitsugu; Miyatani, Katsuya; Takata, Katsuyoshi; Okada, Hiroyuki

    2012-01-01

    A 52-year-old Japanese woman who was eventually diagnosed with primary follicular lymphoma of the duodenum showed atypical endoscopic features, namely, erosions with peripheral whitish edematous mucosa. Initial biopsy specimens taken from the erosions revealed insufficient numbers of lymphoma cells for histological diagnosis. Subsequent biopsy specimens from the peripheral mucosa containing the whitish enlarged villi showed infiltration of the lymphoma cells forming lymphoid follicles, which led us to the appropriate diagnosis. This case indicates that endoscopists should take biopsy samples from the peripheral mucosa with whitish enlarged villi rather than erosions in the rare instances that erosions appear as the main macroscopic feature of intestinal follicular lymphoma. PMID:22690224

  5. Featured Image: Spitzer Galaxies

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2015-11-01

    These three galaxies (click for a full view!) were imaged as a part of the Spitzer Survey of Stellar Structure in Galaxies (S4G), a recent survey of 2352 nearby galaxies with deep imaging at 3.6 and 4.5 m. The bottom panels show false-color near-UV and far-UV images previously obtained with GALEX. The top panels show the new images obtained with Spitzer as part of S4G. The three galaxies shown here represent three types of galaxies that have a high concentration of mass in their centers, yet still have a high specific star-formation rate (the star formation rate per unit stellar mass):Barred galaxies with a prominent ring around their nucleus, like NGC 7552Interacting systems, like NGC 2782Galaxies with compact bulges and smooth extended disks, like NGC 3642To learn why this is the case, and to see more results from S4G, see the original paper below.CitationJuan Carlos Muoz-Mateos et al 2015 ApJS 219 3. doi:10.1088/0067-0049/219/1/3

  6. 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

  7. Atypicality of Atypical Antipsychotics

    PubMed Central

    Farah, Andrew

    2005-01-01

    Objective: To review the current definition of atypicality, discuss the unique features of each atypical antipsychotic, and determine whether the available drugs in this class really meet the classical definition of atypicality. Data Sources: A PubMed search was conducted to identify literature on the subject of this review, supported by additional articles based on the author's clinical knowledge and experience. Study Selection and Data Extraction: Relevant references were extracted and summarized in order to meet the objective of the article. Data Synthesis: Atypical antipsychotics are considered a major advance over conventional antipsychotics, primarily because they offer effective treatment alternatives that are relatively free of extrapyramidal symptoms. In fact, the term atypicality was originally used to describe antipsychotic agents with a minimal risk of causing extrapyramidal symptoms. However, over the years the definition has been modified such that there is currently no consensus on a true definition of atypicality for these agents. Each of the atypical antipsychotics (clozapine, risperidone, olanzapine, quetiapine, ziprasidone, and aripiprazole) commercially available in the United States is unique in terms of its pharmacologic profile, differing with respect to receptor-binding affinity, mechanism of action, and adverse events. Of the available atypical antipsychotics, clozapine and quetiapine have shown the lowest propensity to cause extrapyramidal symptoms. Although the risk of extra-pyramidal symptoms is lower with risperidone and olanzapine than with conventional antipsychotics, risk increases with dose escalation. Data for ziprasidone indicate that the risk of extrapyramidal symptoms may be similar to that of risperidone and olanzapine. There is a concern of akathisia with aripiprazole; however, more experience with this agent is needed before definitive conclusions are made. Conclusion: If the definition of “atypical” antipsychotic is

  8. Atypical patterns in portable monitoring for sleep apnoea: features of nocturnal epilepsy?

    PubMed

    Parrino, Liborio; Milioli, Giulia; Grassi, Andrea; De Paolis, Fernando; Riccardi, Silvia; Colizzi, Elena; Bosi, Marcello; Terzano, Mario Giovanni

    2013-02-01

    Atypical cardiorespiratory patterns can be found during routine clinical use of portable monitoring for diagnosis of sleep-disordered breathing (SDB). Over 1,000 consecutive portable recordings were analysed to study the potential ictal nature of stereotyped cardiorespiratory and motor patterns. Snoring, airflow, thoracic effort, pulse rate, body position, oxygen saturation and activity of the anterior tibialis muscles were quantified. Recordings showing stereotyped polygraphic patterns recurring throughout the night, but without the features of sleep apnoea (apnoea/hypopnoea index <5 events·h(-1)), were selected for investigation. Once included in the study, patients underwent attended nocturnal video polysomnography. A total of 15 recordings showing repeated polygraphic patterns characterised by a sequence of microphone activation, respiratory activity atypical for sleep and wakefulness, heart rate acceleration and limb movements, followed by body position change, were selected for investigation. Once included in the study, patients underwent attended nocturnal video polysomnography that showed frontal epileptic discharges triggering periodic electroencephalographic arousals, autonomic activation and stereotyped motor patterns. A diagnosis of nocturnal frontal lobe epilepsy (NFLE) was established for all patients. NFLE should be taken into consideration in patients with stereotyped and recurrent behavioural features during portable monitoring carried out for diagnosis of SDB. PMID:22700842

  9. 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

  10. 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.

  11. Diagnostic Value of I-131 NP-59 SPECT/CT Scintigraphy in Patients with Subclinical or Atypical Features of Primary Aldosteronism

    PubMed Central

    Chen, Yi-Chun; Su, Yu-Chieh; Wei, Chang-Kuo; Chiu, Jainn-Shiun; Tseng, Chih-En; Chen, Shao-Jer; Wang, Yuh-Feng

    2011-01-01

    Accumulating evidence has shown the adverse effect of long-term hyperaldosteronism on cardiovascular morbidity that is independent of blood pressure. However, the diagnosis of primary aldosteronism (PA) remains a challenge for patients who present with subtle or atypical features or have chronic kidney disease (CKD). SPECT/CT has proven valuable in the diagnosis of a number of conditions. The aim of this study was to determine the usefulness of I-131 NP-59 SPECT/CT in patients with atypical presentations of PA and in those with CKD. The records of 15 patients with PA were retrospectively analyzed. NP-59 SPECT/CT was able to identify adrenal lesion(s) in CKD patients with suspected PA. Patients using NP-59 SPECT/CT imaging, compared with those not performing this procedure, significantly featured nearly normal serum potassium levels, normal aldosterone-renin ratio, and smaller adrenal size on CT and pathological examination and tended to feature stage 1 hypertension and non-suppressed plasma renin activity. These findings show that noninvasive NP-59 SPECT/CT is a useful tool for diagnosis in patients with subclinical or atypical features of PA and those with CKD. PMID:21541242

  12. Depressed older patients with the atypical features of interpersonal rejection sensitivity and reversed-vegetative symptoms are similar to younger atypical patients

    PubMed Central

    Sachs-Ericsson, Natalie; Selby, Edward; Corsentino, Elizabeth; Collins, Nicole; Sawyer, Kathryn; Hames, Jennifer; Arce, Darleine; Joiner, Thomas; Steffens, David C.

    2012-01-01

    Objectives The atypical depression (AD) subtype has rarely been examined in older patients. However, younger AD patients have been characterized as having more severe and chronic symptoms of depression compared with non-AD patients. Design Secondary data analysis using ANOVAs and Growth Curve Modeling. Setting Clinical Research Center for the Study of Depression in Later Life. Participants Depressed older patients (N=248) followed over 2 years. Method In a longitudinal study, we examined depression severity and chronicity in patients with major depression with some features of atypical depression, specifically rejection sensitivity and reversed-vegetative symptoms (e.g., hyperphagia, hypersomnia), or leaden paralysis, and compared them to non-AD patients. The Diagnostic Interview Schedule (DIS) was used to assess depressive symptoms and history. Depression severity and chronicity were assessed every three months using the Montgomery Asberg Depression Rating Scale. Results The AD symptom group reported more DIS depressive symptoms, more thoughts about wanting to die, earlier age of onset, poorer social support and double the number of lifetime episodes than non-AD patients. Growth curve analyses revealed that, compared with non-AD patients, the AD symptom group had more residual symptoms of depression during the first year of follow-up, but not during the second year. Conclusion Characteristics of older patients with features of AD are similar to younger patients. Assessment of atypical symptoms, in particular rejection sensitivity and reversed-vegetative symptoms is essential, and should be considered in treatment plans. PMID:21997599

  13. Gastrointestinal stromal tumours (GISTs) with a thousand faces: atypical manifestations and causes of misdiagnosis on imaging.

    PubMed

    Kim, S W; Kim, H C; Yang, D M; Won, K Y

    2016-02-01

    Gastrointestinal stromal tumours (GISTs) can lead to emergency situations, such as gastrointestinal bleeding, intestinal obstruction, and tumoural rupture with haemoperitoneum or peritonitis. In addition, if a GIST grows exophytically to a large size, it is often misdiagnosed as a tumour arising from adjacent organs. Sometimes, the atypical appearance of GISTs on imaging causes diagnostic confusion. In this article, we illustrate a variety of GISTs with atypical presentations and also discuss the important diagnostic clues for differentiating GISTs from other lesions. PMID:26646370

  14. 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

  15. 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

  16. Efficient image representations and features

    NASA Astrophysics Data System (ADS)

    Dorr, Michael; Vig, Eleonora; Barth, Erhardt

    2013-03-01

    Interdisciplinary research in human vision and electronic imaging has greatly contributed to the current state of the art in imaging technologies. Image compression and image quality are prominent examples and the progress made in these areas relies on a better understanding of what natural images are and how they are perceived by the human visual system. A key research question has been: given the (statistical) properties of natural images, what are the most efficient and perceptually relevant image representations, what are the most prominent and descriptive features of images and videos? We give an overview of how these topics have evolved over the 25 years of HVEI conferences and how they have influenced the current state of the art. There are a number of striking parallels between human vision and electronic imaging. The retina does lateral inhibition, one of the early coders was using a Laplacian pyramid; primary visual cortical areas have orientation- and frequency-selective neurons, the current JPEG standard defines similar wavelet transforms; the brain uses a sparse code, engineers are currently excited about sparse coding and compressed sensing. Some of this has indeed happened at the HVEI conferences and we would like to distill that.

  17. Textural features for image classification

    NASA Technical Reports Server (NTRS)

    Haralick, R. M.; Dinstein, I.; Shanmugam, K.

    1973-01-01

    Description of some easily computable textural features based on gray-tone spatial dependances, and illustration of their application in category-identification tasks of three different kinds of image data - namely, photomicrographs of five kinds of sandstones, 1:20,000 panchromatic aerial photographs of eight land-use categories, and ERTS multispectral imagery containing several land-use categories. Two kinds of decision rules are used - one for which the decision regions are convex polyhedra (a piecewise-linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89% for the photomicrographs, 82% for the aerial photographic imagery, and 83% for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.

  18. Novel Mutation of the GNE Gene Presenting Atypical Mild Clinical Feature: A Korean Case Report.

    PubMed

    Choi, Young-Ah; Park, Sung-Hye; Yi, Youbin; Kim, Keewon

    2015-06-01

    Glucosamine (UDP-N-acetyl)-2-epimerase/N-acetylmannosamine kinase (GNE) myopathy is caused by mutations in GNE, a key enzyme in sialic acid biosynthesis. Here, we reported a case of GNE that presented with atypical mild clinical feature and slow progression. A 48-year-old female had a complaint of left foot drop since the age of 46 years. Electromyography (EMG) and muscle biopsy from left tibialis anterior muscle were compatible with myopathy. Genetic analysis led to the identification of c.1714G>C/c.527A>T compound heterozygous mutation, which is the second most frequent mutation in Japan as far as we know. Previous research has revealed that c.1714G>C/c.527A>T compound heterozygous mutation is a mild mutation as the onset of the disease is much later than the usual age of onset of GNE myopathy and the clinical course is slowly progressive. This was the first case report in Korea of the clinicopathological characteristics of GNE myopathy with GNE (c.1714G>C/c.527A>T compound heterozygous) mutation. PMID:26161358

  19. Extramedullary haematopoiesis: radiological imaging features.

    PubMed

    Roberts, A S; Shetty, A S; Mellnick, V M; Pickhardt, P J; Bhalla, S; Menias, C O

    2016-09-01

    Extramedullary haematopoiesis (EMH) is defined as the production of blood cells outside of the bone marrow, which occurs when there is inadequate production of blood cells. The most common causes of EMH are myelofibrosis, diffuse osseous metastatic disease replacing the bone marrow, leukaemia, sickle cell disease, and thalassemia. The purpose of this article is to review the common and uncommon imaging appearances of EMH by anatomical compartment. In the thorax, EMH most commonly presents as paravertebral fat-containing masses, and typically does not present a diagnostic dilemma; however, EMH in the abdomen most commonly manifests as hepatosplenomegaly with or without focal soft-tissue masses in the liver, spleen, perirenal space, and in the peritoneum. Hepatosplenomegaly, a non-specific feature, most often occurs without an associated focal mass, which makes suggestion of EMH difficult. EMH manifesting as visceral soft-tissue masses often requires biopsy as the differential diagnosis can include lymphoma, metastatic disease, and sarcoma. Many of these soft-tissue masses do not contain adipose elements, making the diagnosis of EMH difficult. Clinical history is crucial, as EMH would likely not otherwise be in the differential in patients with non-specific abdominal masses. Careful biopsy planning is necessary when EMH is a diagnostic consideration, given the propensity for haemorrhage. Understanding the typical imaging appearances of EMH based on its site of manifestation can help the radiologist when encountered with a finding that is diagnostic for EMH, and can help the radiologist suggest the need and plan appropriately for image-guided biopsy. PMID:27377325

  20. The Utility of Gadoxetic Acid-Enhanced MR Imaging to Characterize Atypical Cirrhotic Nodules Detected on Dynamic CT Images

    PubMed Central

    Chou, Chen-Te; Wu, Wen-Pei; Chen, Chia-Bang; Su, Wei-Wen; Chen, Ran-Chou; Chen, Yao-Li

    2014-01-01

    Purpose To evaluate whether gadoxetic acid (Gd-EOB-DTPA)-enhanced MR images of tumors taken during the hepatocyte-specific phase can aid in the differentiation between hepatocellular carcinoma (HCC) and dysplastic nodules (DNs) in patients with atypical cirrhotic nodules detected on dynamic CT images. Materials and Methods Seventy-one patients with 112 nodules showing atypical dynamic enhancement on CT images underwent gadoxetic acid-enhanced MR imaging (MRI) studies. Using a reference standard, we determined that 33 of the nodules were DNs and that 79 were true HCCs. Tumor size, signal intensity on precontrast T1-weighted images (T1WI) and T2WI, and the pattern of dynamic enhancement on MR images taken in the hepatocyte-phase were determined. Results There were significant differences in tumor size, hyperintensity on T2WI, hypointensity on T1WI, typical HCC enhancement pattern on dynamic MR images, or hypointensity on hepatocyte-phase images between DNs and HCC. The sensitivity and specificity were 60.8% and 87.9% for T2WI, 38.0% and 87.9% for T1WI, 17.7% and 100% for dynamic MR imaging, 83.5% and 84.9% for hepatocyte-phase imaging, and 60.8% and 87.9% for tumor size (threshold of 1.7 cm). Conclusion Gd-EOB-DTPA-enhanced hepatocyte-phase imaging is recommended for patients at high risk of HCC who present with atypical lesions on dynamic CT images. PMID:25310817

  1. Atypical presentation of Lemierre syndrome: role of imaging

    PubMed Central

    Kumar, Manoj; Singh, Ragini; Sawlani, Kamal Kumar; Kumar, Santosh

    2013-01-01

    A 51-year-old male patient presented with breathlessness for 10 days. Chest radiograph revealed bilateral moderate pleural effusion. Ultrasound-guided diagnostic pleural aspirate revealed sterile transudative fluid. CT thorax revealed bilateral moderate pleural effusion with partial collapse of both lower lobes and thrombus in right brachiocephalic vein. Two-dimensional-echo revealed circumferential pericardial effusion with mild pericardial thickening and moderate tricuspid regurgitation. Cardiolipin antibodies were within normal limits. d-Dimer assay and C reactive protein were markedly raised. During the period of investigations, the patient had developed mild swelling and pain in right upper limb for which colour Doppler ultrasonography of his right upper limb and neck regions were done. Thrombi in right internal jugular, subclavian and brachiocephalic veins were noted. CT angiography, CT abdomen and chest confirmed the above findings. However, extent of the thrombus and lung lesions was better delineated by CT angiography. We have highlighted the pathognomonic imaging findings of Lemierre syndrome. PMID:23345481

  2. Image fusion using sparse overcomplete feature dictionaries

    SciTech Connect

    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.

  3. Atypical hemispheric asymmetries for the processing of phonological features in children with rolandic epilepsy.

    PubMed

    Bedoin, Nathalie; Ferragne, Emmanuel; Lopez, Céline; Herbillon, Vania; De Bellescize, J; des Portes, Vincent

    2011-05-01

    We assessed language lateralization in 177 healthy 4- to 11-year-old children and adults and atypical asymmetries associated with unilateral epileptic foci in 18 children with benign epilepsy with centrotemporal spikes (BECTS). Dichotic listening results revealed two indices of immature functional asymmetry when the focus was left-sided (BECTS-L). First, children with BECTS-L did not show left hemisphere dominance for the processing of place of articulation, which was recorded in children with BECTS-R and control children. On the contrary, healthy children exhibited a gradual increase in left hemisphere dominance for place processing during childhood, which is consistent with the shift from global to finer-grained acoustic analysis predicted by the Developmental Weighting Shift model. Second, children with BECTS-L showed atypical left hemisphere involvement in the processing of the voiced value (+V), associated with a long acoustic event in French stop consonants, whereas right hemisphere dominance increased with age for +V processing in healthy children. BECTS-L, therefore, interferes with the development of left hemisphere dominance for specific phonological mechanisms. PMID:21470917

  4. 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

  5. Humane images: visual rhetoric in depictions of atypical genital anatomy and sex differentiation.

    PubMed

    Wall, Shelley

    2010-12-01

    Visual images are widely used in medical and patient education to enhance spoken or written explanations. This paper considers the role of such illustrations in shaping conceptions of the body; specifically, it addresses depictions of variant sexual anatomy and their part in the discursive production of intersex bodies. Visual language--even didactic, 'factual' visual language--carries latent as well as manifest content, and influences self-perceptions and social attitudes. In the case of illustrations about atypical sex development, where the need for non-stigmatising communication is crucial, it is especially important to consider the implicit messages conveyed by imagery and compositional strategies. PMID:21393287

  6. Retinal image quality assessment using generic features

    NASA Astrophysics Data System (ADS)

    Fasih, Mahnaz; Langlois, J. M. Pierre; Ben Tahar, Houssem; Cheriet, Farida

    2014-03-01

    Retinal image quality assessment is an important step in automated eye disease diagnosis. Diagnosis accuracy is highly dependent on the quality of retinal images, because poor image quality might prevent the observation of significant eye features and disease manifestations. A robust algorithm is therefore required in order to evaluate the quality of images in a large database. We developed an algorithm for retinal image quality assessment based on generic features that is independent from segmentation methods. It exploits the local sharpness and texture features by applying the cumulative probability of blur detection metric and run-length encoding algorithm, respectively. The quality features are combined to evaluate the image's suitability for diagnosis purposes. Based on the recommendations of medical experts and our experience, we compared a global and a local approach. A support vector machine with radial basis functions was used as a nonlinear classifier in order to classify images to gradable and ungradable groups. We applied our methodology to 65 images of size 2592×1944 pixels that had been graded by a medical expert. The expert evaluated 38 images as gradable and 27 as ungradable. The results indicate very good agreement between the proposed algorithm's predictions and the medical expert's judgment: the sensitivity and specificity for the local approach are respectively 92% and 94%. The algorithm demonstrates sufficient robustness to identify relevant images for automated diagnosis.

  7. Adenoma or atypical hepatic focal nodular hyperplasia: role of preoperative imaging and laparoscopic treatment.

    PubMed

    Di Carlo, Isidoro; Pulvirenti, Elia; Toro, Adriana; Priolo, Gian Domenico

    2010-06-01

    Differentiation of focal nodular hyperplasia (FNH) and other hypervascular liver lesions, such as hepatocellular adenoma (HCA), is important because of the drastically different therapeutic approach. However, FNH can be well distinguished only if it shows a typical aspect; alternatively, in the case of atypical FNH, imaging findings are not specific enough to provide a secure diagnosis and histologic verification of the lesion is required. In addition, HCA cannot be identified conclusively by any current available imaging technique and it can be at best suspected strongly, and this suspicion may lead to liver resection. Herein we report a case of a patient with an unusual FNH nodule presenting at ultrasonographic scanning as an isoechoic mass arising from hepatic segment 4b; the diagnostic indecision between FNH and HCA was not definitively solved even after computed tomography scan and magnetic resonance imaging and the patient was scheduled for a laparoscopic resection. The pathologic examination diagnosed an atypical FNH nodule. The clinical doubt between FNH and HCA remains a problem affecting the clinicians, and more effort should be made in the direction of a better preoperative differentiation of such different conditions. Surgical resection should not be considered as the failure of the preoperative diagnostic attempt, but as the mainstay for a definitive and sure diagnosis. PMID:20551788

  8. 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.

  9. 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

  10. Automatic Extraction of Planetary Image Features

    NASA Technical Reports Server (NTRS)

    Troglio, G.; LeMoigne, J.; Moser, G.; Serpico, S. B.; Benediktsson, J. A.

    2009-01-01

    With the launch of several Lunar missions such as the Lunar Reconnaissance Orbiter (LRO) and Chandrayaan-1, a large amount of Lunar images will be acquired and will need to be analyzed. Although many automatic feature extraction methods have been proposed and utilized for Earth remote sensing images, these methods are not always applicable to Lunar data that often present low contrast and uneven illumination characteristics. In this paper, we propose a new method for the extraction of Lunar features (that can be generalized to other planetary images), based on the combination of several image processing techniques, a watershed segmentation and the generalized Hough Transform. This feature extraction has many applications, among which image registration.

  11. 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.

  12. 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.

  13. 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.

  14. 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

  15. Extraction of edge feature in cardiovascular image

    NASA Astrophysics Data System (ADS)

    Lu, Jianrong; Chen, Dongqing; Yu, Daoyin; Liu, Xiaojun

    2001-09-01

    Extraction of edge feature and accurate measurement of vascular diameter in cardiovascular image are the bases for labeling the coronary hierarchy, 3D refined reconstruction of the coronary arterial tree and accurate fusion between the calculated 3D vascular trees and other views. In order to extract vessels from the image, the grayscale minimization of the circle template and differential edge detection are put forward. Edge pixels of the coronary artery are set according to maximization of the differential value. The edge lines are determined after the edge pixels are smoothed by B-Spline function. The assessment of feature extraction is demonstrated by the excellent performance in computer simulation and actual application.

  16. 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.

  17. Different mutations at V363 MAPT codon are associated with atypical clinical phenotypes and show unusual structural and functional features.

    PubMed

    Rossi, Giacomina; Bastone, Antonio; Piccoli, Elena; Morbin, Michela; Mazzoleni, Giulia; Fugnanesi, Valeria; Beeg, Marten; Del Favero, Elena; Cantù, Laura; Motta, Simona; Salsano, Ettore; Pareyson, Davide; Erbetta, Alessandra; Elia, Antonio Emanuele; Del Sorbo, Francesca; Silani, Vincenzo; Morelli, Claudia; Salmona, Mario; Tagliavini, Fabrizio

    2014-02-01

    Microtubule-associated protein tau gene (MAPT) is one of the major genes linked to frontotemporal lobar degeneration, a group of neurodegenerative diseases clinically, pathologically, and genetically heterogeneous. In particular, MAPT mutations give rise to the subgroup of tauopathies. The pathogenetic mechanisms underlying the MAPT mutations so far described are the decreased ability of tau protein to promote microtubule polymerization (missense mutations) or the altered ratio of tau isoforms (splicing mutations), both leading to accumulation of hyperphosphorylated filamentous tau protein. Following a genetic screening of patients affected by frontotemporal lobar degeneration, we identified 2 MAPT mutations, V363I and V363A, leading to atypical clinical phenotypes, such as posterior cortical atrophy. We investigated in vitro features of the recombinant mutated tau isoforms and revealed unusual functional and structural characteristics such as an increased ability to promote microtubule polymerization and a tendency to form oligomeric instead of filamentous aggregates. Thus, we disclosed a greater than expected complexity of abnormal features of mutated tau isoforms. Overall our findings suggest a high probability that these mutations are pathogenic. PMID:24018212

  18. Operational observations of atypical meteorological features using the WSR-88D

    SciTech Connect

    Tongue, J.S.; Lehenbauer, G.J.; Michael, P.A.; Miller, M.A.

    1996-09-01

    The Weather Surveillance Radar-1988 Doppler (WSR-88D) provides invaluable information on a variety of meteorological phenomena. The high sensitivity of the WSR-88D allows for the observation of phenomena that were not observable with previous WSR`s. Precipitation phase, land-sea breeze circulations, and clouds are examples of phenomena that are now observable by the WSR-88D. The detection of these features has an enormous impact on forecast operations.

  19. Recurrent Wernicke's Encephalopathy in a 16-Year-Old Girl with Atypical Clinical and Radiological Features

    PubMed Central

    Lamdhade, S.; Almulla, A.; Alroughani, R.

    2014-01-01

    Background. Wernicke's Encephalopathy (WE) is a clinical diagnosis with serious neurological consequences. Its occurrence is underestimated in nonalcoholics and is uncommon in adolescents. We aim to draw the attention to a rare case, which had additional clinical and radiological features. Case. A 16-year-old girl presented with three-week history of vomiting secondary to intestinal obstruction. She developed diplopia soon after hospitalization. Neurological evaluation revealed restriction of bilateral lateral recti with horizontal nystagmus, and bilateral limb dysmetria. Brain MRI was normal. She had prompt improvement to thiamine. Four months later, she presented with headache, bilateral severe deafness, and tinnitus. Clinically, she had severe sensorineural hearing loss, bilateral lateral recti paresis, and gait ataxia. CT head showed bilateral caudate nucleus hypodensities. MRI brain revealed gadolinium enhancement of mamillary bodies and vermis. She had significant improvement after IV thiamine. Headache completely resolved while the ocular movements, hearing, and tinnitus improved partially in 72 hours. Conclusions. Recurrent WE in adolescence is uncommon. Headache, tinnitus, and deafness are rare clinical features. Although MRI study shows typical features of WE, the presence of bilateral caudate nuclei hypodensities on CT scan is uncommon. Prompt treatment with thiamine is warranted in suspected cases to prevent permanent neurological sequelae. PMID:24790762

  20. Multimodality imaging features of hereditary multiple exostoses

    PubMed Central

    Fitzgerald, L; Campbell, N; Lyburn, I D; Munk, P L; Buckley, O; Torreggiani, W C

    2013-01-01

    Hereditary multiple exostoses (HME) or diaphyseal aclasis is an inherited disorder characterised by the formation of multiple osteochondromas, which are cartilage-capped osseous outgrowths, and the development of associated osseous deformities. Individuals with HME may be asymptomatic or develop clinical symptoms, which prompt imaging studies. Different modalities ranging from plain radiographs to cross-sectional and nuclear medicine imaging studies can be helpful in the diagnosis and detection of complications in HME, including chondrosarcomatous transformation. We review the role and imaging features of these different modalities in HME. PMID:24004486

  1. Role of the Wada test and functional magnetic resonance imaging in preoperative mapping of language and memory: two atypical cases.

    PubMed

    Połczyńska, Monika M; Benjamin, Christopher F A; Moseley, Brian D; Walshaw, Patricia; Eliashiv, Dawn; Vigil, Celia; Jones, Michael; Bookheimer, Susan Y

    2015-01-01

    The Wada test is an invasive procedure used to determine cerebral memory and language dominance as well as risk of cognitive deficits following neurosurgery. However, the potential risks of Wada testing have led some to consider foregoing Wada testing in candidates for resective epilepsy surgery with right hemispheric seizure onset. We present two atypical cases in which the Wada test showed unexpected memory and language lateralization. These cases underscore the importance of functional magnetic resonance in which imaging and Wada examination in right-handed individuals even when the lesion would not suggest atypical language representation. PMID:25372664

  2. 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

  3. 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

  4. Hyperspectral image feature extraction accelerated by GPU

    NASA Astrophysics Data System (ADS)

    Qu, HaiCheng; Zhang, Ye; Lin, Zhouhan; Chen, Hao

    2012-10-01

    PCA (principal components analysis) algorithm is the most basic method of dimension reduction for high-dimensional data1, which plays a significant role in hyperspectral data compression, decorrelation, denoising and feature extraction. With the development of imaging technology, the number of spectral bands in a hyperspectral image is getting larger and larger, and the data cube becomes bigger in these years. As a consequence, operation of dimension reduction is more and more time-consuming nowadays. Fortunately, GPU-based high-performance computing has opened up a novel approach for hyperspectral data processing6. This paper is concerning on the two main processes in hyperspectral image feature extraction: (1) calculation of transformation matrix; (2) transformation in spectrum dimension. These two processes belong to computationally intensive and data-intensive data processing respectively. Through the introduction of GPU parallel computing technology, an algorithm containing PCA transformation based on eigenvalue decomposition 8(EVD) and feature matching identification is implemented, which is aimed to explore the characteristics of the GPU parallel computing and the prospects of GPU application in hyperspectral image processing by analysing thread invoking and speedup of the algorithm. At last, the result of the experiment shows that the algorithm has reached a 12x speedup in total, in which some certain step reaches higher speedups up to 270 times.

  5. 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

  6. 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

  7. Multispectral image fusion based on fractal features

    NASA Astrophysics Data System (ADS)

    Tian, Jie; Chen, Jie; Zhang, Chunhua

    2004-01-01

    Imagery sensors have been one indispensable part of the detection and recognition systems. They are widely used to the field of surveillance, navigation, control and guide, et. However, different imagery sensors depend on diverse imaging mechanisms, and work within diverse range of spectrum. They also perform diverse functions and have diverse circumstance requires. So it is unpractical to accomplish the task of detection or recognition with a single imagery sensor under the conditions of different circumstances, different backgrounds and different targets. Fortunately, the multi-sensor image fusion technique emerged as important route to solve this problem. So image fusion has been one of the main technical routines used to detect and recognize objects from images. While, loss of information is unavoidable during fusion process, so it is always a very important content of image fusion how to preserve the useful information to the utmost. That is to say, it should be taken into account before designing the fusion schemes how to avoid the loss of useful information or how to preserve the features helpful to the detection. In consideration of these issues and the fact that most detection problems are actually to distinguish man-made objects from natural background, a fractal-based multi-spectral fusion algorithm has been proposed in this paper aiming at the recognition of battlefield targets in the complicated backgrounds. According to this algorithm, source images are firstly orthogonally decomposed according to wavelet transform theories, and then fractal-based detection is held to each decomposed image. At this step, natural background and man-made targets are distinguished by use of fractal models that can well imitate natural objects. Special fusion operators are employed during the fusion of area that contains man-made targets so that useful information could be preserved and features of targets could be extruded. The final fused image is reconstructed from the

  8. Feature-Linking Model for Image Enhancement.

    PubMed

    Zhan, Kun; Teng, Jicai; Shi, Jinhui; Li, Qiaoqiao; Wang, Mingying

    2016-06-01

    Inspired by gamma-band oscillations and other neurobiological discoveries, neural networks research shifts the emphasis toward temporal coding, which uses explicit times at which spikes occur as an essential dimension in neural representations. We present a feature-linking model (FLM) that uses the timing of spikes to encode information. The first spiking time of FLM is applied to image enhancement, and the processing mechanisms are consistent with the human visual system. The enhancement algorithm achieves boosting the details while preserving the information of the input image. Experiments are conducted to demonstrate the effectiveness of the proposed method. Results show that the proposed method is effective. PMID:26942747

  9. 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

  10. Treatment with cimetidine of atypical fasciitis panniculitis syndrome.

    PubMed Central

    Naschitz, J E; Yeshurun, D; Rosner, I; Abrahamson, J E; Misselevitch, I; Boss, J H

    1990-01-01

    Three patients presented with septal fasciitis and panniculitis, associated with clinical and laboratory features which precluded straight-forward classification into eosinophilic fasciitis, localised scleroderma, or lupus erythematosus profundus. Treatment with cimetidine caused the remission of cutaneous manifestations and the extracutaneous abnormalities, such as nailfold capillary disturbances and the presence of antithyroid antibodies, improved. It is concluded that features of eosinophilic fasciitis or localised scleroderma and certain additional atypical elements should be categorised as atypical fasciitis-panniculitis syndrome. Images PMID:2241270

  11. 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…

  12. Feature Evaluation for Building Facade Images - AN Empirical Study

    NASA Astrophysics Data System (ADS)

    Yang, M. Y.; Förstner, W.; Chai, D.

    2012-08-01

    The classification of building facade images is a challenging problem that receives a great deal of attention in the photogrammetry community. Image classification is critically dependent on the features. In this paper, we perform an empirical feature evaluation task for building facade images. Feature sets we choose are basic features, color features, histogram features, Peucker features, texture features, and SIFT features. We present an approach for region-wise labeling using an efficient randomized decision forest classifier and local features. We conduct our experiments with building facade image classification on the eTRIMS dataset, where our focus is the object classes building, car, door, pavement, road, sky, vegetation, and window.

  13. A hybrid features based image matching algorithm

    NASA Astrophysics Data System (ADS)

    Tu, Zhenbiao; Lin, Tao; Sun, Xiao; Dou, Hao; Ming, Delie

    2015-12-01

    In this paper, we present a novel image matching method to find the correspondences between two sets of image interest points. The proposed method is based on a revised third-order tensor graph matching method, and introduces an energy function that takes four kinds of energy term into account. The third-order tensor method can hardly deal with the situation that the number of interest points is huge. To deal with this problem, we use a potential matching set and a vote mechanism to decompose the matching task into several sub-tasks. Moreover, the third-order tensor method sometimes could only find a local optimum solution. Thus we use a cluster method to divide the feature points into some groups and only sample feature triangles between different groups, which could make the algorithm to find the global optimum solution much easier. Experiments on different image databases could prove that our new method would obtain correct matching results with relatively high efficiency.

  14. 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

  15. 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

  16. 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

  17. Atypical pneumonia

    MedlinePlus

    ... that cause typical pneumonia. These include Legionella pneumophila , Mycoplasma pneumoniae , and Chlamydophila pneumoniae . Atypical pneumonia also tends to have milder symptoms than typical pneumonia. Causes Mycoplasma pneumonia is a type of atypical pneumonia. It ...

  18. Toward Automated Feature Detection in UAVSAR Images

    NASA Astrophysics Data System (ADS)

    Parker, J. W.; Donnellan, A.; Glasscoe, M. T.

    2014-12-01

    Edge detection identifies seismic or aseismic fault motion, as demonstrated in repeat-pass inteferograms obtained by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) program. But this identification is not robust at present: it requires a flattened background image, interpolation into missing data (holes) and outliers, and background noise that is either sufficiently small or roughly white Gaussian. Identification and mitigation of nongaussian background image noise is essential to creating a robust, automated system to search for such features. Clearly a robust method is needed for machine scanning of the thousands of UAVSAR repeat-pass interferograms for evidence of fault slip, landslides, and other local features.Empirical examination of detrended noise based on 20 km east-west profiles through desert terrain with little tectonic deformation for a suite of flight interferograms shows nongaussian characteristics. Statistical measurement of curvature with varying length scale (Allan variance) shows nearly white behavior (Allan variance slope with spatial distance from roughly -1.76 to -2) from 25 to 400 meters, deviations from -2 suggesting short-range differences (such as used in detecting edges) are often freer of noise than longer-range differences. At distances longer than 400 m the Allan variance flattens out without consistency from one interferogram to another. We attribute this additional noise afflicting difference estimates at longer distances to atmospheric water vapor and uncompensated aircraft motion.Paradoxically, California interferograms made with increasing time intervals before and after the El Mayor Cucapah earthquake (2008, M7.2, Mexico) show visually stronger and more interesting edges, but edge detection methods developed for the first year do not produce reliable results over the first two years, because longer time spans suffer reduced coherence in the interferogram. The changes over time are reflecting fault slip and block

  19. 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.

  20. Variants of meningiomas: a review of imaging findings and clinical features.

    PubMed

    Kunimatsu, Akira; Kunimatsu, Natsuko; Kamiya, Kouhei; Katsura, Masaki; Mori, Harushi; Ohtomo, Kuni

    2016-07-01

    Meningiomas are common neoplasms that frequently occur in the brain and spine. Among the 15 histological subtypes of meningiomas in the WHO classification, the incidence of meningothelial meningiomas is the highest, followed by fibrous and transitional meningiomas. These three subtypes account for approximately 80 % of all meningiomas, and thus could be regarded as typical meningiomas. For this reason, other uncommon histological subtypes may be considered as imaging variants, and diagnosis is often challenging for radiologists solely based on imaging features of typical meningiomas. In addition to the histological subtypes, meningiomas arising in atypical locations could be easily mistaken for other lesions more commonly observed in those locations. The purpose of this article is to review characteristic clinical and imaging findings of uncommon meningiomas, including histological variants and meningiomas occurring in relatively rare locations. PMID:27138052

  1. 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.

  2. Investigation of atypical molten pool dynamics in tungsten carbide-cobalt during laser deposition using in-situ thermal imaging

    SciTech Connect

    Xiong Yuhong; Schoenung, Julie M.; Hofmeister, William H.; Smugeresky, John E.; Delplanque, Jean-Pierre

    2012-01-16

    An atypical ''swirling'' phenomenon observed during the laser deposition of tungsten carbide-cobalt cermets by laser engineered net shaping (LENS) was studied using in-situ high-speed thermal imaging. To provide fundamental insight into this phenomenon, the thermal behavior of pure cobalt during LENS was also investigated for comparison. Several factors were considered as the possible source of the observed differences. Of those, phase difference, material emissivity, momentum transfer, and free surface disruption from the powder jets, and, to a lesser extent, Marangoni convection were identified as the relevant mechanisms.

  3. Atypical Optic Neuritis.

    PubMed

    Gaier, Eric D; Boudreault, Katherine; Rizzo, Joseph F; Falardeau, Julie; Cestari, Dean M

    2015-12-01

    Classic demyelinative optic neuritis is associated with multiple sclerosis and typically carries a good prognosis for visual recovery. This disorder is well characterized with respect to its presentation and clinical features by baseline data obtained through the optic neuritis treatment trial and numerous other studies. Atypical optic neuritis entails clinical manifestations that deviate from this classic pattern of features. Clinical signs and symptoms that deviate from the typical presentation should prompt consideration of less common etiologies. Atypical features to consider include lack of pain, simultaneous or near-simultaneous onset, lack of response to or relapse upon tapering from corticosteroids, or optic nerve head or peripapillary hemorrhages. The most important alternative etiologies to consider and the steps towards their respective diagnostic evaluations are suggested for these atypical features. PMID:26467052

  4. Evaluation of textural features for multispectral images

    NASA Astrophysics Data System (ADS)

    Bayram, Ulya; Can, Gulcan; Duzgun, Sebnem; Yalabik, Nese

    2011-11-01

    Remote sensing is a field that has wide use, leading to the fact that it has a great importance. Therefore performance of selected features plays a great role. In order to gain some perspective on useful textural features, we have brought together state-of-art textural features in recent literature, yet to be applied in remote sensing field, as well as presenting a comparison with traditional ones. Therefore we selected most commonly used textural features in remote sensing that are grey-level co-occurrence matrix (GLCM) and Gabor features. Other selected features are local binary patterns (LBP), edge orientation features extracted after applying steerable filter, and histogram of oriented gradients (HOG) features. Color histogram feature is also used and compared. Since most of these features are histogram-based, we have compared performance of bin-by-bin comparison with a histogram comparison method named as diffusion distance method. During obtaining performance of each feature, k-nearest neighbor classification method (k-NN) is applied.

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

    SciTech Connect

    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.

  6. 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.

  7. [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

  8. 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.

  9. 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.

  10. Computer detection of features in biomedical images

    SciTech Connect

    Not Available

    1993-05-01

    Two projects under way at LLNL require the detection of spots in biomedical images: physical mapping of DNA in chromosomes, for the Human Genome Project, and finding microcalcifications, which may be an early sign of breast cancer, in mammograms. We have developed several computational algorithms to analyze these two kinds of images. The two detection methods described here use morphological imaging techniques to obtain size, shape, texture, and other information inherent in am image without trying to fit the data to a rigid mathematical model. The spot-finding algorithm has been incorporated into a DNA mapping tool for chromosomes in the metaphase of cell division; it is heavily used by researchers at the University of California, San Francisco, and may soon be distributed to other universities. Our computerized mammography work is in progress; when completed, we plan to transfer the technology to a medical imaging company.

  11. 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.

  12. Atypical Mole (Atypical Nevus)

    MedlinePlus

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  13. 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

  14. Visible and infrared image registration based on visual salient features

    NASA Astrophysics Data System (ADS)

    Wu, Feihong; Wang, Bingjian; Yi, Xiang; Li, Min; Hao, Jingya; Qin, Hanlin; Zhou, Huixin

    2015-09-01

    In order to improve the precision of visible and infrared (VIS/IR) image registration, an image registration method based on visual salient (VS) features is presented. First, a VS feature detector based on the modified visual attention model is presented to extract VS points. Because the iterative, within-feature competition method used in visual attention models is time consuming, an alternative fast visual salient (FVS) feature detector is proposed to make VS features more efficient. Then, a descriptor-rearranging (DR) strategy is adopted to describe feature points. This strategy combines information of both IR image and its negative image to overcome the contrast reverse problem between VIS and IR images, making it easier to find the corresponding points on VIS/IR images. Experiments show that both VS and FVS detectors have higher repeatability scores than scale invariant feature transform in the cases of blurring, brightness change, JPEG compression, noise, and viewpoint, except big scale change. The combination of VS detector and DR registration strategy can achieve precise image registration, but it is time-consuming. The combination of FVS detector and DR registration strategy can also reach a good registration of VIS/IR images but in a shorter time.

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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.

  1. Cell nuclear features for classification from fluorescence images

    NASA Astrophysics Data System (ADS)

    Heynen, Susanne; Hunter, Edward; Price, Jeffrey H.

    2000-04-01

    In clinical cytology, nuclear features play an important role in cell and tissue classification. To increase efficiency and decrease subjectivity of cytological results, automation of the analytic process has been proposed and discussed by many authors. This automation can be achieved by estimating the probability of occurrence of a certain class given particular features of a microscope specimen. In this paper, feature sets that might be used as inputs for mathematical cytological classification algorithms are reviewer. The primary goal was to determine the important properties of these features sets, i.e., are there mathematically efficient features that provide a more or less compete description of the cell. Under what conditions will these feature then result in optimal classification of the cells using quantitative fluorescence staining. And how would these mathematical features relate to conventional features that a human observer understands. Example human observer features are size, shape, and chromaticity o the cell nucleus while example mathematical features are image moments. If the cell image can be completely reconstructed from the feature set, then it should be possible to derive the conventional features used by human observers from the mathematical feature set for presentation to clinicians. Finally, the suitability of different mathematical decision making algorithms like probabilistic reasoning, clustering or neural networks are also briefly evaluated in the context of a mathematically complete feature set.

  2. 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

  3. 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.

  4. 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.

  5. 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.

  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. 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

  8. 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

  9. 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

  10. 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.

  11. An adaptive multi-feature segmentation model for infrared image

    NASA Astrophysics Data System (ADS)

    Zhang, Tingting; Han, Jin; Zhang, Yi; Bai, Lianfa

    2016-04-01

    Active contour models (ACM) have been extensively applied to image segmentation, conventional region-based active contour models only utilize global or local single feature information to minimize the energy functional to drive the contour evolution. Considering the limitations of original ACMs, an adaptive multi-feature segmentation model is proposed to handle infrared images with blurred boundaries and low contrast. In the proposed model, several essential local statistic features are introduced to construct a multi-feature signed pressure function (MFSPF). In addition, we draw upon the adaptive weight coefficient to modify the level set formulation, which is formed by integrating MFSPF with local statistic features and signed pressure function with global information. Experimental results demonstrate that the proposed method can make up for the inadequacy of the original method and get desirable results in segmenting infrared images.

  12. Assist features: placement, impact, and relevance for EUV imaging

    NASA Astrophysics Data System (ADS)

    Mochi, Iacopo; Philipsen, Vicky; Gallagher, Emily; Hendrickx, Eric; Lyakhova, Kateryna; Wittebrood, Friso; Schiffelers, Guido; Fliervoet, Timon; Wang, Shibing; Hsu, Stephen; Plachecki, Vince; Baron, Stan; Laenens, Bart

    2016-03-01

    Assist features are commonly used in DUV lithography to improve the lithographic process window of isolated features under illumination conditions that enable the printability of dense features. With the introduction of EUV lithography, the interaction between 13.5 nm light and the mask features generates strong mask 3D effects. On wafer, the mask 3D effects manifest as pitch-dependent best focus positions, pattern asymmetries and image contrast loss. To minimize the mask 3D effects, and enhance the lithographic process window, we explore by means of wafer print evaluation the use of assist features with different sizes and placements. The assist features are placed next to isolated features and two bar structures, consistent with theN5 (imec iN7) node dimensions for 0.33NA and we use different types of off-axis illumination . For the generic iN7 structures, wafer imaging will be compared to simulation results and an assessment of optimal assist feature configuration will be made. It is also essential to understand the potential benefit of using assist features and to weigh that benefit against the price of complexity associated with adding sub-resolution features on a production mask. To that end, we include an OPC study that compares a layout treated with assist features, to one without assist features, using full-chip complexity metrics like data size.

  13. Image mosaic method based on SIFT features of line segment.

    PubMed

    Zhu, Jun; Ren, Mingwu

    2014-01-01

    This paper proposes a novel image mosaic method based on SIFT (Scale Invariant Feature Transform) feature of line segment, aiming to resolve incident scaling, rotation, changes in lighting condition, and so on between two images in the panoramic image mosaic process. This method firstly uses Harris corner detection operator to detect key points. Secondly, it constructs directed line segments, describes them with SIFT feature, and matches those directed segments to acquire rough point matching. Finally, Ransac method is used to eliminate wrong pairs in order to accomplish image mosaic. The results from experiment based on four pairs of images show that our method has strong robustness for resolution, lighting, rotation, and scaling. PMID:24511326

  14. 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).

  15. 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

  16. 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.

  17. Retinal image registration via feature-guided Gaussian mixture model.

    PubMed

    Liu, Chengyin; Ma, Jiayi; Ma, Yong; Huang, Jun

    2016-07-01

    Registration of retinal images taken at different times, from different perspectives, or with different modalities is a critical prerequisite for the diagnoses and treatments of various eye diseases. This problem can be formulated as registration of two sets of sparse feature points extracted from the given images, and it is typically solved by first creating a set of putative correspondences and then removing the false matches as well as estimating the spatial transformation between the image pairs or solved by estimating the correspondence and transformation jointly involving an iteration process. However, the former strategy suffers from missing true correspondences, and the latter strategy does not make full use of local appearance information, which may be problematic for low-quality retinal images due to a lack of reliable features. In this paper, we propose a feature-guided Gaussian mixture model (GMM) to address these issues. We formulate point registration as the estimation of a feature-guided mixture of densities: A GMM is fitted to one point set, such that both the centers and local features of the Gaussian densities are constrained to coincide with the other point set. The problem is solved under a unified maximum-likelihood framework together with an iterative expectation-maximization algorithm initialized by the confident feature correspondences, where the image transformation is modeled by an affine function. Extensive experiments on various retinal images show the robustness of our approach, which consistently outperforms other state-of-the-art methods, especially when the data is badly degraded. PMID:27409682

  18. The analysis of image feature robustness using cometcloud

    PubMed Central

    Qi, Xin; Kim, Hyunjoo; Xing, Fuyong; Parashar, Manish; Foran, David J.; Yang, Lin

    2012-01-01

    The robustness of image features is a very important consideration in quantitative image analysis. The objective of this paper is to investigate the robustness of a range of image texture features using hematoxylin stained breast tissue microarray slides which are assessed while simulating different imaging challenges including out of focus, changes in magnification and variations in illumination, noise, compression, distortion, and rotation. We employed five texture analysis methods and tested them while introducing all of the challenges listed above. The texture features that were evaluated include co-occurrence matrix, center-symmetric auto-correlation, texture feature coding method, local binary pattern, and texton. Due to the independence of each transformation and texture descriptor, a network structured combination was proposed and deployed on the Rutgers private cloud. The experiments utilized 20 randomly selected tissue microarray cores. All the combinations of the image transformations and deformations are calculated, and the whole feature extraction procedure was completed in 70 minutes using a cloud equipped with 20 nodes. Center-symmetric auto-correlation outperforms all the other four texture descriptors but also requires the longest computational time. It is roughly 10 times slower than local binary pattern and texton. From a speed perspective, both the local binary pattern and texton features provided excellent performance for classification and content-based image retrieval. PMID:23248759

  19. 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.

  20. 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. PMID:26241988

  1. 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

  2. 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

  3. 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

  4. 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

  5. Three-dimensional floating images as overt security features

    NASA Astrophysics Data System (ADS)

    Dunn, Douglas S.; Potts, Travis L.; Lorimor, Lynn E.; Jonza, James M.; Smithson, Robert M.; Maki, Stephen P.

    2006-02-01

    3M has developed a proprietary laser process for creating three-dimensional images that appear to float above and/or below the plane of a substrate containing an array of microlenses. During the imaging process the laser records a microscopic image of the desired three-dimensional pattern in the material located at the focal point of each microlens in the array. The images exhibit motion parallax comparable to that seen from holograms and are easily visible in a wide range of ambient lighting conditions. The images are therefore similar, but not identical, to integral images, first proposed in 1908 by Lippmann. The fidelity of these floating images requires maintaining exact registration between the microlens array and the corresponding microimage array. In addition, the use of an ablative laser process for the production of the microimages enables the production of microimage features smaller than the diffraction limit (up to approximately 50,000 dpi). The images are therefore very difficult to simulate, counterfeit, or modify and are highly desirable as an overt security feature. 3M has scaled up the floating image process to produce images in Confirm TM Retroreflective Security Laminate to authenticate passports and driver's licenses and in retroreflective license plate sheeting as the Ensure TM Virtual Security Thread to authenticate vehicle registration. This allows addition of features to a secure document that are easily verifiable, using only the human eye, by a large and widely disperse population to create an identity document that is easily identified as genuine.

  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. 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

  8. Thermal imaging of sedimentary features on alluvial fans

    NASA Astrophysics Data System (ADS)

    Hardgrove, Craig; Moersch, Jeffrey; Whisner, Stephen

    2010-03-01

    Aerial thermal imaging is used to study grain-size distributions and induration on a wide variety of alluvial fans in the desert southwest of the United States. High-resolution aerial thermal images reveal evidence of sedimentary processes that rework and build alluvial fans, as preserved in the grain-size distributions and surface induration those processes leave behind. A catalog of constituent sedimentary features that can be identified using aerial thermal and visible imaging is provided. These features include clast-rich and clast-poor debris flows, incised channel deposits, headward-eroding gullies, sheetflood, lag surfaces, active/inactive lobes, distal sand-skirts and basin-related salt pans. Ground-based field observations of surface grain-size distributions, as well as morphologic, cross-cutting and topographic relationships were used to confirm the identifications of these feature types in remotely acquired thermal and visible images. Thermal images can also reveal trends in grain sizes between neighboring alluvial fans on a regional scale. Although inferences can be made using thermal images alone, the results from this study demonstrate that a more thorough geological interpretation of sedimentary features on an alluvial fan can be made using a combination of thermal and visible images. The results of this study have potential applications for Mars, where orbital thermal imaging might be used as a tool for evaluating constituent sedimentary processes on proposed alluvial fans.

  9. 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.

  10. 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.

  11. 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

  12. 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

  13. Improvements in Intrinsic Feature Pose Measurement for Awake Animal Imaging

    SciTech Connect

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

    2010-01-01

    Development has continued with intrinsic feature optical motion tracking for awake animal imaging to measure 3D position and orientation (pose) for motion compensated reconstruction. Prior imaging results have been directed towards head motion measurement for SPECT brain studies in awake unrestrained mice. This work improves on those results in extracting and tracking intrinsic features from multiple camera images and computing pose changes from the tracked features over time. Previously, most motion tracking for 3D imaging has been limited to measuring extrinsic features such as retro-reflective markers applied to an animal s head. While this approach has been proven to be accurate, the use of external markers is undesirable for several reasons. The intrinsic feature approach has been further developed from previous work to provide full pose measurements for a live mouse scan. Surface feature extraction, matching, and pose change calculation with point tracking and accuracy results are described. Experimental pose calculation and 3D reconstruction results from live images are presented.

  14. Improvements in intrinsic feature pose measurement for awake animal imaging

    SciTech Connect

    J.S. Goddard, J.S. Baba, S.J. Lee, A.G. Weisenberger, A. Stolin, J. McKisson, M.F. Smith

    2011-06-01

    Development has continued with intrinsic feature optical motion tracking for awake animal imaging to measure 3D position and orientation (pose) for motion compensated reconstruction. Prior imaging results have been directed towards head motion measurement for SPECT brain studies in awake unrestrained mice. This work improves on those results in extracting and tracking intrinsic features from multiple camera images and computing pose changes from the tracked features over time. Previously, most motion tracking for 3D imaging has been limited to measuring extrinsic features such as retro-reflective markers applied to an animal's head. While this approach has been proven to be accurate, the use of external markers is undesirable for several reasons. The intrinsic feature approach has been further developed from previous work to provide full pose measurements for a live mouse scan. Surface feature extraction, matching, and pose change calculation with point tracking and accuracy results are described. Experimental pose calculation and 3D reconstruction results from live images are presented.

  15. 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.

  16. 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.

  17. 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.

  18. Face Recognition with Multi-Resolution Spectral Feature Images

    PubMed Central

    Sun, Zhan-Li; Lam, Kin-Man; Dong, Zhao-Yang; Wang, Han; Gao, Qing-Wei; Zheng, Chun-Hou

    2013-01-01

    The one-sample-per-person problem has become an active research topic for face recognition in recent years because of its challenges and significance for real-world applications. However, achieving relatively higher recognition accuracy is still a difficult problem due to, usually, too few training samples being available and variations of illumination and expression. To alleviate the negative effects caused by these unfavorable factors, in this paper we propose a more accurate spectral feature image-based 2DLDA (two-dimensional linear discriminant analysis) ensemble algorithm for face recognition, with one sample image per person. In our algorithm, multi-resolution spectral feature images are constructed to represent the face images; this can greatly enlarge the training set. The proposed method is inspired by our finding that, among these spectral feature images, features extracted from some orientations and scales using 2DLDA are not sensitive to variations of illumination and expression. In order to maintain the positive characteristics of these filters and to make correct category assignments, the strategy of classifier committee learning (CCL) is designed to combine the results obtained from different spectral feature images. Using the above strategies, the negative effects caused by those unfavorable factors can be alleviated efficiently in face recognition. Experimental results on the standard databases demonstrate the feasibility and efficiency of the proposed method. PMID:23418451

  19. Predicting beef tenderness using color and multispectral image texture features.

    PubMed

    Sun, X; Chen, K J; Maddock-Carlin, K R; Anderson, V L; Lepper, A N; Schwartz, C A; Keller, W L; Ilse, B R; Magolski, J D; Berg, E P

    2012-12-01

    The objective of this study was to investigate the usefulness of raw meat surface characteristics (texture) in predicting cooked beef tenderness. Color and multispectral texture features, including 4 different wavelengths and 217 image texture features, were extracted from 2 laboratory-based multispectral camera imaging systems. Steaks were segregated into tough and tender classification groups based on Warner-Bratzler shear force. The texture features were submitted to STEPWISE multiple regression and support vector machine (SVM) analyses to establish prediction models for beef tenderness. A subsample (80%) of tender or tough classified steaks were used to train models which were then validated on the remaining (20%) test steaks. For color images, the SVM model correctly identified tender steaks with 100% accurately while the STEPWISE equation identified 94.9% of the tender steaks correctly. For multispectral images, the SVM model predicted 91% and STEPWISE predicted 87% average accuracy of beef tender. PMID:22647652

  20. 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.

  1. 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. PMID:26513781

  2. 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.

  3. 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.

  4. 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. PMID:27002328

  5. Optimal feature extraction for segmentation of Diesel spray images.

    PubMed

    Payri, Francisco; Pastor, José V; Palomares, Alberto; Juliá, J Enrique

    2004-04-01

    A one-dimensional simplification, based on optimal feature extraction, of the algorithm based on the likelihood-ratio test method (LRT) for segmentation in colored Diesel spray images is presented. If the pixel values of the Diesel spray and the combustion images are represented in RGB space, in most cases they are distributed in an area with a given so-called privileged direction. It is demonstrated that this direction permits optimal feature extraction for one-dimensional segmentation in the Diesel spray images, and some of its advantages compared with more-conventional one-dimensional simplification methods, including considerably reduced computational cost while accuracy is maintained within more than reasonable limits, are presented. The method has been successfully applied to images of Diesel sprays injected at room temperature as well as to images of sprays with evaporation and combustion. It has proved to be valid for several cameras and experimental arrangements. PMID:15074419

  6. 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

  7. 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

  8. Coevolving feature extraction agents for target recognition in SAR images

    NASA Astrophysics Data System (ADS)

    Bhanu, Bir; Krawiec, Krzysztof

    2003-09-01

    This paper describes a novel evolutionary method for automatic induction of target recognition procedures from examples. The learning process starts with training data containing SAR images with labeled targets and consists in coevolving the population of feature extraction agents that cooperate to build an appropriate representation of the input image. Features extracted by a team of cooperating agents are used to induce a machine learning classifier that is responsible for making the final decision of recognizing a target in a SAR image. Each agent (individual) contains feature extraction procedure encoded according to the principles of linear genetic programming (LGP). Like 'plain' genetic programming, in LGP an agent's genome encodes a program that is executed and tested on the set of training images during the fitness calculation. The program is a sequence of calls to the library of parameterized operations, including, but not limited to, global and local image processing operations, elementary feature extraction, and logic and arithmetic operations. Particular calls operate on working variables that enable the program to store intermediate results, and therefore design complex features. This paper contains detailed description of the learning and recognition methodology outlined here. In experimental part, we report and analyze the results obtained when testing the proposed approach for SAR target recognition using MSTAR database.

  9. Image Recommendation Algorithm Using Feature-Based Collaborative Filtering

    NASA Astrophysics Data System (ADS)

    Kim, Deok-Hwan

    As the multimedia contents market continues its rapid expansion, the amount of image contents used in mobile phone services, digital libraries, and catalog service is increasing remarkably. In spite of this rapid growth, users experience high levels of frustration when searching for the desired image. Even though new images are profitable to the service providers, traditional collaborative filtering methods cannot recommend them. To solve this problem, in this paper, we propose feature-based collaborative filtering (FBCF) method to reflect the user's most recent preference by representing his purchase sequence in the visual feature space. The proposed approach represents the images that have been purchased in the past as the feature clusters in the multi-dimensional feature space and then selects neighbors by using an inter-cluster distance function between their feature clusters. Various experiments using real image data demonstrate that the proposed approach provides a higher quality recommendation and better performance than do typical collaborative filtering and content-based filtering techniques.

  10. 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

  11. 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.

  12. Suprasellar masses in children: Characteristic MR imaging features.

    PubMed

    Yildiz, Adalet Elcin; Oguz, Kader Karli; Fitoz, Suat

    2016-07-01

    Pediatric suprasellar masses are unique in their clinical presentation and imaging features. The differential diagnosis, incidence, surgical approach and adjuvant treatment options differ from adult tumors. Magnetic resonance (MR) imaging is fundamental in preoperative evaluation and provides detailed information about the suprasellar region. In this article, we review the characteristic MR imaging findings of common suprasellar masses in children. We also briefly discuss useful MR imaging sequences and planes used in the initial evaluation of a pediatric suprasellar mass and clinical findings at presentation. PMID:27131616

  13. 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.

  14. 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.

  15. Atypical histiocytosis in the lung

    PubMed Central

    Cao, Weijun; Zhang, Rongxuan; Zhou, Ying; Xu, Jinfu; Garfield, David H.

    2013-01-01

    Objective To report a rare case of atypical histiocytic tumor of the lung with a review of literature. Methods The clinical materials were noted. Literature related to this condition from the past 50 years was reviewed from the group of histiocytic tumors. Results and conclusions Clinical manifestations were non-specific. The imaging characteristics of our case were infiltrative lesions with multiple cysts in both lungs. Pathology showed nodular proliferation of atypical cells. Immunohistochemistry suggested a histiocytic origin of the infiltrating atypical cells. Because the pathological findings did not fall into any particular category of typical histiocytic tumors, the final diagnosis was atypical histiocytic tumor. The presentation of atypical histiocytic tumor of the lungs, only, with infiltrative lesions and multiple air cysts seems very rare, with pathological examination being “gold standard” for the diagnosis. PMID:23991320

  16. 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.

  17. 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.

  18. Modeling the statistics of image features and associated text

    NASA Astrophysics Data System (ADS)

    Barnard, Kobus; Duygulu, Pinar; Forsyth, David A.

    2001-12-01

    We present a methodology for modeling the statistics of image features and associated text in large datasets. The models used also serve to cluster the images, as images are modeled as being produced by sampling from a limited number of combinations of mixing components. Furthermore, because our approach models the joint occurrence image features and associated text, it can be used to predict the occurrence of either, based on observations or queries. This supports an attractive approach to image search as well as novel applications such a suggesting illustrations for blocks of text (auto-illustrate) and generating words for images outside the training set (auto-annotate). In this paper we illustrate the approach on 10,000 images of work from the Fine Arts Museum of San Francisco. The images include line drawings, paintings, and pictures of sculpture and ceramics. Many of the images have associated free text whose nature varies greatly, from physical description to interpretation and mood. We incorporate statistical natural language processing in order to deal with free text. We use WordNet to provide semantic grouping information and to help disambiguate word senses, as well as emphasize the hierarchical nature of semantic relationships.

  19. Improved image retrieval based on fuzzy colour feature vector

    NASA Astrophysics Data System (ADS)

    Ben-Ahmeida, Ahlam M.; Ben Sasi, Ahmed Y.

    2013-03-01

    One of Image indexing techniques is the Content-Based Image Retrieval which is an efficient way for retrieving images from the image database automatically based on their visual contents such as colour, texture, and shape. In this paper will be discuss how using content-based image retrieval (CBIR) method by colour feature extraction and similarity checking. By dividing the query image and all images in the database into pieces and extract the features of each part separately and comparing the corresponding portions in order to increase the accuracy in the retrieval. The proposed approach is based on the use of fuzzy sets, to overcome the problem of curse of dimensionality. The contribution of colour of each pixel is associated to all the bins in the histogram using fuzzy-set membership functions. As a result, the Fuzzy Colour Histogram (FCH), outperformed the Conventional Colour Histogram (CCH) in image retrieving, due to its speedy results, where were images represented as signatures that took less size of memory, depending on the number of divisions. The results also showed that FCH is less sensitive and more robust to brightness changes than the CCH with better retrieval recall values.

  20. 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.

  1. The neural representation of typical and atypical experiences of negative images: comparing fear, disgust and morbid fascination.

    PubMed

    Oosterwijk, Suzanne; Lindquist, Kristen A; Adebayo, Morenikeji; Barrett, Lisa Feldman

    2016-01-01

    Negative stimuli do not only evoke fear or disgust, but can also evoke a state of 'morbid fascination' which is an urge to approach and explore a negative stimulus. In the present neuroimaging study, we applied an innovative method to investigate the neural systems involved in typical and atypical conceptualizations of negative images. Participants received false feedback labeling their mental experience as fear, disgust or morbid fascination. This manipulation was successful; participants judged the false feedback correct for 70% of the trials on average. The neuroimaging results demonstrated differential activity within regions in the 'neural reference space for discrete emotion' depending on the type of feedback. We found robust differences in the ventrolateral prefrontal cortex, the dorsomedial prefrontal cortex and the lateral orbitofrontal cortex comparing morbid fascination to control feedback. More subtle differences in the dorsomedial prefrontal cortex and the lateral orbitofrontal cortex were also found between morbid fascination feedback and the other emotion feedback conditions. This study is the first to forward evidence about the neural representation of the experimentally unexplored state of morbid fascination. In line with a constructionist framework, our findings suggest that neural resources associated with the process of conceptualization contribute to the neural representation of this state. PMID:26180088

  2. Volumetric feature extraction and visualization of tomographic molecular imaging.

    PubMed

    Bajaj, Chandrajit; Yu, Zeyun; Auer, Manfred

    2003-01-01

    Electron tomography is useful for studying large macromolecular complex within their cellular context. The associate problems include crowding and complexity. Data exploration and 3D visualization of complexes require rendering of tomograms as well as extraction of all features of interest. We present algorithms for fully automatic boundary segmentation and skeletonization, and demonstrate their applications in feature extraction and visualization of cell and molecular tomographic imaging. We also introduce an interactive volumetric exploration and visualization tool (Volume Rover), which encapsulates implementations of the above volumetric image processing algorithms, and additionally uses efficient multi-resolution interactive geometry and volume rendering techniques for interactive visualization. PMID:14643216

  3. Investigation of image feature extraction by a genetic algorithm

    NASA Astrophysics Data System (ADS)

    Brumby, Steven P.; Theiler, James P.; Perkins, Simon J.; Harvey, Neal R.; Szymanski, John J.; Bloch, Jeffrey J.; Mitchell, Melanie

    1999-11-01

    We describe the implementation and performance of a genetic algorithm which generates image feature extraction algorithms for remote sensing applications. We describe our basis set of primitive image operators and present our chromosomal representation of a complete algorithm. Our initial application has been geospatial feature extraction using publicly available multi-spectral aerial-photography data sets. We present the preliminary results of our analysis of the efficiency of the classic genetic operations of crossover and mutation for our application, and discuss our choice of evolutionary control parameters. We exhibit some of our evolved algorithms, and discuss possible avenues for future progress.

  4. Hyperspectral image classifier based on beach spectral feature

    NASA Astrophysics Data System (ADS)

    Liang, Zhang; Lianru, Gao; Bing, Zhang

    2014-03-01

    The seashore, especially coral bank, is sensitive to human activities and environmental changes. A multispectral image, with coarse spectral resolution, is inadaptable for identify subtle spectral distinctions between various beaches. To the contrary, hyperspectral image with narrow and consecutive channels increases our capability to retrieve minor spectral features which is suit for identification and classification of surface materials on the shore. Herein, this paper used airborne hyperspectral data, in addition to ground spectral data to study the beaches in Qingdao. The image data first went through image pretreatment to deal with the disturbance of noise, radiation inconsistence and distortion. In succession, the reflection spectrum, the derivative spectrum and the spectral absorption features of the beach surface were inspected in search of diagnostic features. Hence, spectra indices specific for the unique environment of seashore were developed. According to expert decisions based on image spectrums, the beaches are ultimately classified into sand beach, rock beach, vegetation beach, mud beach, bare land and water. In situ surveying reflection spectrum from GER1500 field spectrometer validated the classification production. In conclusion, the classification approach under expert decision based on feature spectrum is proved to be feasible for beaches.

  5. 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.

  6. 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

  7. Atypical Trigeminal Neuralgia Secondary to Meningioma.

    PubMed

    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

  8. The fuzzy Hough Transform-feature extraction in medical images

    SciTech Connect

    Philip, K.P.; Dove, E.L.; Stanford, W.; Chandran, K.B. ); McPherson, D.D.; Gotteiner, N.L. . Dept. of Internal Medicine)

    1994-06-01

    Identification of anatomical features is a necessary step for medical image analysis. Automatic methods for feature identification using conventional pattern recognition techniques typically classify an object as a member of a predefined class of objects, but do not attempt to recover the exact or approximate shape of that object. For this reason, such techniques are usually not sufficient to identify the borders of organs when individual geometry varies in local detail, even though the general geometrical shape is similar. The authors present an algorithm that detects features in an image based on approximate geometrical models. The algorithm is based on the traditional and generalized Hough Transforms but includes notions from fuzzy set theory. The authors use the new algorithm to roughly estimate the actual locations of boundaries of an internal organ, and from this estimate, to determine a region of interest around the organ. Based on this rough estimate of the border location, and the derived region of interest, the authors find the final estimate of the true borders with other image processing techniques. The authors present results that demonstrate that the algorithm was successfully used to estimate the approximate location of the chest wall in humans, and of the left ventricular contours of a dog heart obtained from cine-computed tomographic images. The authors use this fuzzy Hough Transform algorithm as part of a larger procedures to automatically identify the myocardial contours of the heart. This algorithm may also allow for more rapid image processing and clinical decision making in other medical imaging applications.

  9. Radiological Features of Hepatocellular Carcinoma

    PubMed Central

    Shah, Samir; Shukla, Akash; Paunipagar, Bhawan

    2014-01-01

    Present article is a review of radiological features of hepatocellular carcinoma on various imaging modalities. With the advancement in imaging techniques, biopsy is rarely needed for diagnosis of hepatocellular carcinoma (HCC), unlike other malignancies. Imaging is useful not only for diagnosis but also for surveillance, therapy and assessing response to treatment. The classical and the atypical radiological features of HCC have been described. PMID:25755613

  10. Investigation of efficient features for image recognition by neural networks.

    PubMed

    Goltsev, Alexander; Gritsenko, Vladimir

    2012-04-01

    In the paper, effective and simple features for image recognition (named LiRA-features) are investigated in the task of handwritten digit recognition. Two neural network classifiers are considered-a modified 3-layer perceptron LiRA and a modular assembly neural network. A method of feature selection is proposed that analyses connection weights formed in the preliminary learning process of a neural network classifier. In the experiments using the MNIST database of handwritten digits, the feature selection procedure allows reduction of feature number (from 60 000 to 7000) preserving comparable recognition capability while accelerating computations. Experimental comparison between the LiRA perceptron and the modular assembly neural network is accomplished, which shows that recognition capability of the modular assembly neural network is somewhat better. PMID:22391231

  11. Texture Features from Mammographic Images and Risk of Breast Cancer

    PubMed Central

    Manduca, Armando; Carston, Michael J.; Heine, John J.; Scott, Christopher G.; Pankratz, V. Shane; Brandt, Kathy R.; Sellers, Thomas A.; Vachon, Celine M.; Cerhan, James R.

    2009-01-01

    Mammographic percent density (PD) is a strong risk factor for breast cancer, but there has been relatively little systematic evaluation of other features in mammographic images that might additionally predict breast cancer risk. We evaluated the association of a large number of image texture features with risk of breast cancer using a clinic-based case-control study of digitized film mammograms, all with screening mammograms prior to breast cancer diagnosis. The sample was split into training (123 cases, 258 controls) and validation (123 cases, 264 controls) datasets. Age and body mass index (BMI)-adjusted Odds Ratios (ORs) per standard deviation change in the feature, 95% confidence intervals, and the area under the receiver operator characteristic curve (AUC) were obtained using logistic regression. A bootstrap approach was used to identify the strongest features in the training dataset, and results for features that validated in the second half of the sample were reported using the full dataset. The mean age at mammography was 64.0 ± 10.2 years, and the mean time from mammography to breast cancer was 3.7 ± 1.0 (range 2.0-5.9 years). PD was associated with breast cancer risk (OR=1.49; 1.25-1.78). The strongest features that validated from each of several classes (Markovian, run-length, Laws, wavelet and Fourier) showed similar ORs as PD and predicted breast cancer at a similar magnitude (AUC=0.58-0.60) as PD (AUC=0.58). All of these features were automatically calculated (unlike PD), and measure texture at a coarse scale. These features were moderately correlated with PD (r = 0.39-0.64), and after adjustment for PD, each of the features attenuated only slightly and retained statistical significance. However, simultaneous inclusion of these features in a model with PD did not significantly improve the ability to predict breast cancer. PMID:19258482

  12. Recognition and diagnosis of atypical depression.

    PubMed

    Thase, Michael E

    2007-01-01

    The term atypical depression dates to the first wave of reports describing differential response to monoamine oxidase inhibitors (MAOIs) and tricyclic antidepressants (TCAs). In contrast to more TCA-responsive depressions, patients with so-called atypical symptoms (e.g., hypersomnia, interpersonal sensitivity, leaden paralysis, increased appetite and/or weight, and phobic anxiety) were observed to be more responsive to MAOIs. After several decades of controversy and debate, the phrase "with atypical features" was added as an episode specifier in the DSM-IV in 1994. The 1-year prevalence of the defined atypical depression subtype is approximately 1% to 4%; around 15% to 29% of patients with major depressive disorder have atypical depression. Hardly "atypical" in contemporary contexts, atypical depression also is common in dysthymic bipolar II disorders and is notable for its early age at onset, more chronic course, and high rates of comorbidity with social phobia and panic disorder with agoraphobia. The requirement of preserved mood reactivity is arguably the most controversial of the DSM-IV criteria for atypical depression. When compared with melancholia, the neurobiological profiles of patients with atypical depression are relatively normal. The utility of the atypical depression subtype for differential therapeutics diminished substantially when the TCAs were supplanted as first-line antidepressants by the selective serotonin reuptake inhibitors. Although introduction of safer MAOIs has fostered renewed interest in atypical depression, the validity and importance of the DSM-IV definition of atypical depression for the nosology of affective illness remains an open question. PMID:17640153

  13. Image enhancement techniques applied to solar feature detection

    NASA Astrophysics Data System (ADS)

    Kowalski, Artur J.

    This dissertation presents the development of automatic image enhancement techniques for solar feature detection. The new method allows for detection and tracking of the evolution of filaments in solar images. Series of H-alpha full-disk images are taken in regular time intervals to observe the changes of the solar disk features. In each picture, the solar chromosphere filaments are identified for further evolution examination. The initial preprocessing step involves local thresholding to convert grayscale images into black-and-white pictures with chromosphere granularity enhanced. An alternative preprocessing method, based on image normalization and global thresholding is presented. The next step employs morphological closing operations with multi-directional linear structuring elements to extract elongated shapes in the image. After logical union of directional filtering results, the remaining noise is removed from the final outcome using morphological dilation and erosion with a circular structuring element. Experimental results show that the developed techniques can achieve excellent results in detecting large filaments and good detection rates for small filaments. The final chapter discusses proposed directions of the future research and applications to other areas of solar image processing, in particular to detection of solar flares, plages and sunspots.

  14. Characterizing mammographic images by using generic texture features

    PubMed Central

    2012-01-01

    Introduction Although mammographic density is an established risk factor for breast cancer, its use is limited in clinical practice because of a lack of automated and standardized measurement methods. The aims of this study were to evaluate a variety of automated texture features in mammograms as risk factors for breast cancer and to compare them with the percentage mammographic density (PMD) by using a case-control study design. Methods A case-control study including 864 cases and 418 controls was analyzed automatically. Four hundred seventy features were explored as possible risk factors for breast cancer. These included statistical features, moment-based features, spectral-energy features, and form-based features. An elaborate variable selection process using logistic regression analyses was performed to identify those features that were associated with case-control status. In addition, PMD was assessed and included in the regression model. Results Of the 470 image-analysis features explored, 46 remained in the final logistic regression model. An area under the curve of 0.79, with an odds ratio per standard deviation change of 2.88 (95% CI, 2.28 to 3.65), was obtained with validation data. Adding the PMD did not improve the final model. Conclusions Using texture features to predict the risk of breast cancer appears feasible. PMD did not show any additional value in this study. With regard to the features assessed, most of the analysis tools appeared to reflect mammographic density, although some features did not correlate with PMD. It remains to be investigated in larger case-control studies whether these features can contribute to increased prediction accuracy. PMID:22490545

  15. 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...

  16. 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...

  17. Feature-based multiexposure image-sequence fusion with guided filter and image alignment

    NASA Astrophysics Data System (ADS)

    Xu, Liang; Du, Junping; Zhang, Zhenhong

    2015-01-01

    Multiexposure fusion images have a higher dynamic range and reveal more details than a single captured image of a real-world scene. A clear and intuitive feature-based fusion technique for multiexposure image sequences is conceptually proposed. The main idea of the proposed method is to combine three image features [phase congruency (PC), local contrast, and color saturation] to obtain weight maps of the images. Then, the weight maps are further refined using a guided filter which can improve their accuracy. The final fusion result is constructed using the weighted sum of the source image sequence. In addition, for multiexposure image-sequence fusion involving dynamic scenes containing moving objects, ghost artifacts can easily occur if fusion is directly performed. Therefore, an image-alignment method is first used to adjust the input images to correspond to a reference image, after which fusion is performed. Experimental results demonstrate that the proposed method has a superior performance compared to the existing methods.

  18. Diffusion tensor image registration using tensor geometry and orientation features.

    PubMed

    Yang, Jinzhong; Shen, Dinggang; Davatzikos, Christos; Verma, Ragini

    2008-01-01

    This paper presents a method for deformable registration of diffusion tensor (DT) images that integrates geometry and orientation features into a hierarchical matching framework. The geometric feature is derived from the structural geometry of diffusion and characterizes the shape of the tensor in terms of prolateness, oblateness, and sphericity of the tensor. Local spatial distributions of the prolate, oblate, and spherical geometry are used to create an attribute vector of geometric feature for matching. The orientation feature improves the matching of the WM fiber tracts by taking into account the statistical information of underlying fiber orientations. These features are incorporated into a hierarchical deformable registration framework to develop a diffusion tensor image registration algorithm. Extensive experiments on simulated and real brain DT data establish the superiority of this algorithm for deformable matching of diffusion tensors, thereby aiding in atlas creation. The robustness of the method makes it potentially useful for group-based analysis of DT images acquired in large studies to identify disease-induced and developmental changes. PMID:18982691

  19. Feature extraction from mammographic images using fast marching methods

    NASA Astrophysics Data System (ADS)

    Bottigli, U.; Golosio, B.

    2002-07-01

    Features extraction from medical images represents a fundamental step for shape recognition and diagnostic support. The present work faces the problem of the detection of large features, such as massive lesions and organ contours, from mammographic images. The regions of interest are often characterized by an average grayness intensity that is different from the surrounding. In most cases, however, the desired features cannot be extracted by simple gray level thresholding, because of image noise and non-uniform density of the surrounding tissue. In this work, edge detection is achieved through the fast marching method (Level Set Methods and Fast Marching Methods, Cambridge University Press, Cambridge, 1999), which is based on the theory of interface evolution. Starting from a seed point in the shape of interest, a front is generated which evolves according to an appropriate speed function. Such function is expressed in terms of geometric properties of the evolving interface and of image properties, and should become zero when the front reaches the desired boundary. Some examples of application of such method to mammographic images from the CALMA database (Nucl. Instr. and Meth. A 460 (2001) 107) are presented here and discussed.

  20. 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.

  1. Evaluation of Selected Features for CAR Detection in Aerial Images

    NASA Astrophysics Data System (ADS)

    Tuermer, S.; Leitloff, J.; Reinartz, P.; Stilla, U.

    2011-09-01

    The extraction of vehicles from aerial images provides a wide area traffic situation within a short time. Applications for the gathered data are various and reach from smart routing in the case of congestions to usability validation of roads in the case of disasters. The challenge of the vehicle detection task is finding adequate features which are capable to separate cars from other objects; especially those that look similar. We present an experiment where selected features show their ability of car detection. Precisely, Haar-like and HoG features are utilized and passed to the AdaBoost algorithm for calculating the final detector. Afterwards the classifying power of the features is accurately analyzed and evaluated. The tests a carried out on aerial data from the inner city of Munich, Germany and include small inner city roads with rooftops close by which raise the complexity factor.

  2. 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

  3. Image recognition of diseased rice seeds based on color feature

    NASA Astrophysics Data System (ADS)

    Cheng, Fang; Ying, Yibin

    2004-11-01

    The objective of this research is to develop a digital image analysis algorithm for detection of diseased rice seeds based on color features. The rice seeds used for this study involved five varieties of Jinyou402, Shanyou10, Zhongyou207, Jiayou99 and IIyou3207. Images of rice seeds were acquired with a color machine vision system. Each original RGB image was converted to HSV color space and preprocessed to show, as hue in the seed region while the pixels value of background was zero. The hue values were scaled so that they varied from 0.0 to 1.0. Then six color features were extracted and evaluated for their contributions to seed classification. Determined using Blocks method, the mean hue value shows the strongest classification ability. Parzen windowing function method was used to estimate probability density distribution and a threshold of mean hue was drawn to classify normal seeds and diseased seeds. The average accuracy of test data set is 95% for Jinyou402. Then the feature of hue histogram was extracted for diseased seeds and partitioned into two clusters of spot diseased seeds and severe diseased seeds. Desired results were achieved when the two cancroids locations were used to discriminate the disease degree. Combined with the two features of mean hue and histogram, all seeds could be classified as normal seeds, spot diseased seeds and severe diseased seeds. Finally, the algorithm was implemented for all the five varieties to test the adaptability.

  4. 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.

  5. 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.

  6. [Atypical odontalgia].

    PubMed

    Türp, Jens Christoph

    2005-01-01

    In spite of its first description by the English surgeon JOHN HUNTER more than 200 years ago, atypical odontalgia (AO), or phantom tooth pain, is not universally known among dentists. AO is a persistent neuropathic pain which may be initiated after deafferentiation of trigeminal nerve fibers following root canal treatment, apicectomy, or tooth extraction. In the absence of pathological clinical or radiological findings, the diagnosis is made by exclusion. After a thorough patient education about the condition, pharmacological and psychological pain management is required. Invasive and irreversible treatment attempts are contraindicated. PMID:16342640

  7. Atypical Inflammasomes.

    PubMed

    Janowski, Ann M; Sutterwala, Fayyaz S

    2016-01-01

    Pattern recognition receptors, including members of the NLR and PYHIN families, are essential for recognition of both pathogen- and host-derived danger signals. A number of molecules in these families are capable of forming multiprotein complexes termed inflammasomes that result in the activation of caspase-1. In addition to NLRP1, NLRP3, NLRC4, and AIM2, which form well-described inflammasome complexes, IFI16, NLRP6, NLRP7, NLRP12, and NLRC5 have also been proposed to form inflammasomes under specific conditions. The structure and function of these atypical inflammasomes will be highlighted here. PMID:27221480

  8. Deep Optical Images of Malin 1 Reveal New Features

    NASA Astrophysics Data System (ADS)

    Galaz, Gaspar; Milovic, Carlos; Suc, Vincent; Busta, Luis; Lizana, Guadalupe; Infante, Leopoldo; Royo, Santiago

    2015-12-01

    We present Megacam deep optical images (g and r) of Malin 1 obtained with the 6.5 m Magellan/Clay telescope, detecting structures down to ˜28 B mag arcsec-2. In order to enhance galaxy features buried in the noise, we use a noise reduction filter based on the total generalized variation regularizator. This method allows us to detect and resolve very faint morphological features, including spiral arms, with a high visual contrast. For the first time, we can appreciate an optical image of Malin 1 and its morphology in full view. The images provide unprecedented detail, compared to those obtained in the past with photographic plates and CCD, including Hubble Space Telescope imaging. We detect two peculiar features in the disk/spiral arms. The analysis suggests that the first one is possibly a background galaxy, and the second is an apparent stream without a clear nature, but could be related to the claimed past interaction between Malin 1 and the galaxy SDSSJ123708.91+142253.2. Malin 1 exhibits features suggesting the presence of stellar associations and clumps of molecular gas, not seen before with such a clarity. Using these images, we obtain a diameter for Malin 1 of 160 kpc, ˜50 kpc larger than previous estimates. A simple analysis shows that the observed spiral arms reach very low luminosity and mass surface densities, to levels much lower than the corresponding values for the Milky Way. This paper includes data gathered with the 6.5 meter Magellan Telescopes located at Las Campanas Observatory, Chile.

  9. 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

  10. Imaging features of benign and malignant ampullary and periampullary lesions.

    PubMed

    Nikolaidis, Paul; Hammond, Nancy A; Day, Kevin; Yaghmai, Vahid; Wood, Cecil G; Mosbach, David S; Harmath, Carla B; Taffel, Myles T; Horowitz, Jeanne M; Berggruen, Senta M; Miller, Frank H

    2014-01-01

    The ampulla of Vater is an important anatomic landmark where the common bile duct and main pancreatic duct converge in the major duodenal papilla. Imaging evaluation of the ampulla and periampullary region poses a unique diagnostic challenge to radiologists because of the region's complex and variable anatomy and the variety of lesions that can occur. Lesions intrinsic to the ampulla and involved segment of the biliary tree can be neoplastic, inflammatory, or congenital. Neoplastic lesions include ampullary adenocarcinomas and adenomas, which often are difficult to differentiate, as well as pancreatic or duodenal adenocarcinomas, pancreatic neuroendocrine tumors, and cholangiocarcinomas. Ultrasonography (US), computed tomography, magnetic resonance (MR) imaging, and MR cholangiopancreatography are commonly used to evaluate this region. Endoscopic retrograde cholangiopancreatography or endoscopic US examination may be necessary for more definitive evaluation. Periampullary conditions in the duodenum that may secondarily involve the ampulla include neoplasms, duodenitis, duodenal diverticula, and Brunner's gland hyperplasia or hamartomas. Because these lesions can exhibit a wide overlap of imaging features and subtle or nonspecific imaging findings, diagnosis is made on the basis of patient age, clinical history, and imaging and laboratory findings. Given the complexity of imaging evaluation of the ampulla and periampullary region, it is essential for radiologists to understand the variety of lesions that can occur and recognize their imaging characteristics. PMID:24819785

  11. Adrenal imaging for adenoma characterization: imaging features, diagnostic accuracies and differential diagnoses.

    PubMed

    Park, Jung Jae; Park, Byung Kwan; Kim, Chan Kyo

    2016-06-01

    Adrenocortical adenoma is the most common adrenal tumour. This lesion is frequently encountered on cross-sectional imaging that has been performed for unrelated reasons. Adrenal adenoma manifests various imaging features on CT, MRI and positron emission tomography/CT. The learning objectives of this review are to describe the imaging findings of adrenocortical adenoma, to compare the sensitivities of different imaging modalities for adenoma characterization and to introduce differential diagnoses. PMID:26867466

  12. 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.

  13. 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

  14. 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

  15. The speckle image reconstruction of the solar small scale features

    NASA Astrophysics Data System (ADS)

    Zhong, Libo; Tian, Yu; Rao, Changhui

    2014-11-01

    The resolution of the astronomical object observed by the earth-based telescope is limited due to the atmospheric turbulence. Speckle image reconstruction method provides access to detect small-scale solar features near the diffraction limit of the telescope. This paper describes the implementation of the reconstruction of images obtained by the 1-m new vacuum solar telescope at Full-Shine solar observatory. Speckle masking method is used to reconstruct the Fourier phases for its better dynamic range and resolution capabilities. Except of the phase reconstruction process, several problems encounter in the solar image reconstruction are discussed. The details of the implement including the flat-field, image segmentation, Fried parameter estimation and noise filter estimating are described particularly. It is demonstrated that the speckle image reconstruction is effective to restore the wide field of view images. The qualities of the restorations are evaluated by the contrast ratio. When the Fried parameter is 10cm, the contrast ratio of the sunspot and granulation can be improved from 0.3916 to 0.6845 and from 0.0248 to 0.0756 respectively.

  16. Featured Image: A New Look at Malin 1

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2016-01-01

    Monochrome, inverted version of Malin 1. [Adapted from Galaz et al. 2015]The above image of Malin 1, the faintest and largest low-surface-brightness galaxy ever observed, was obtained with an instrument called Megacam on the 6.5m Magellan/Clay telescope. Gaspar Galaz (Pontifical Catholic University of Chile) and collaborators used Megacam to obtain deep optical observations of Malin 1. They then used novel noise-reduction and image-processing techniques to create this spectacular image of the spiral galaxy located roughly 1.2 billion light-years away. This new view of Malin 1 reveals details weve never before seen, including a stream within the disk that may have been caused by a past interaction between Malin 1 and another galaxy near it. Check outthe image to the rightfor a monochrome, inverted version thatmakes it a little easier to see some of Malin 1s features. To see the full original images and to learn more about what the images reveal about Malin 1, see the paper below.CitationGaspar Galaz et al 2015 ApJ 815 L29. doi:10.1088/2041-8205/815/2/L29

  17. 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.

  18. 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.

  19. Spinal Neuroarthropathy: Pathophysiology, Clinical and Imaging Features, and Differential Diagnosis.

    PubMed

    Ledbetter, Luke N; Salzman, Karen L; Sanders, R Kent; Shah, Lubdha M

    2016-01-01

    Spinal neuroarthropathy (SNA), or Charcot spine, is a progressive destructive arthropathy occurring after loss of neuroprotective sensation and proprioceptive reflexes. Clinical diagnosis is difficult because of the variable length to presentation after initial neurologic damage and the limited symptoms given preexisting neurologic deficits. SNA is also a diagnostic challenge because its imaging features are similar to those of spinal conditions such as discitis-osteomyelitis, osseous tuberculosis, hemodialysis-related spondyloarthropathy, and pseudarthrosis. The most important imaging clues for diagnosis of SNA are involvement of both anterior and posterior elements at the thoracolumbar and lumbosacral junctions. Additional imaging clues include vacuum phenomenon within the disk (indicating excessive motion), malalignment, and paraspinal soft-tissue masses or fluid collections containing bone debris. Despite these imaging signs, findings may overlap in some cases with those of infection, or SNA can be superinfected, and biopsy may be necessary. Development of SNA requires a preexisting neurologic condition, most commonly traumatic spinal cord injury. Areas of greatest mobility and weight bearing within the desensate spine experience repetitive microtrauma and unregulated hyperemia, leading to destruction of the intervertebral articulations. The progressive and destructive nature of SNA causes substantial deformity, loss of function, and often further neurologic deficits. Patients present with deformity, back pain, audible noises during movement, or new neurologic symptoms. The mainstay of treatment is surgical débridement, reduction, and fusion. The radiologist can help initiate early intervention by using key imaging features to distinguish SNA from imaging mimics and prevent further neurologic deterioration. (©)RSNA, 2016. PMID:27058729

  20. Shape Adaptive, Robust Iris Feature Extraction from Noisy Iris Images

    PubMed Central

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

    2013-01-01

    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

  1. 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

  2. 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

  3. 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

  4. 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.

  5. New features for detecting cervical precancer using hyperspectral diagnostic imaging

    NASA Astrophysics Data System (ADS)

    Okimoto, Gordon S.; Parker, Mary F.; Mooradian, Gregory C.; Saggese, Steven J.; Grisanti, Ames A.; O'Connor, Dennis M.; Miyazawa, Kunio

    2001-05-01

    Principal component analysis (PCA) in the wavelet domain provides powerful new features for the non-invasive detection of cervical intraepithelial neoplasia (CIN) using fluorescence imaging spectroscopy. These features are known as principal wavelet components (PWCs). The multiscale structure of the fluorescence spectrum for each pixel of the hyperspectral data cube is extracted using the continuous wavelet transform. PCA is then used to compress and denoise the wavelet representation for presentation to a feed- forward neural network for tissue classification. Using PWC features as inputs to a 5-class NN resulted in average correct classification rates of 95% over five cervical tissue classes corresponding to low-grade dysplasia, squamous, columnar, metaplasia plus a fifth class for other unspecified tissue types, blood and mucus. A 2-class NN was also trained to discriminate between CIN1 and normal tissue with sensitivity and specificity of 98% and 99%, respectively. All performance assessments were based on test data from a set of patients not seen during NN training. Trained neural classifiers were used to `compress' and transform 3D hyperspectral data cubes into 2D color-coded images that accurately mapped the spatial distribution of both normal and dysplastic tissue over the surface of the entire cervix.

  6. 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.

  7. 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

  8. Automatic archaeological feature extraction from satellite VHR images

    NASA Astrophysics Data System (ADS)

    Jahjah, Munzer; Ulivieri, Carlo

    2010-05-01

    Archaeological applications need a methodological approach on a variable scale able to satisfy the intra-site (excavation) and the inter-site (survey, environmental research). The increased availability of high resolution and micro-scale data has substantially favoured archaeological applications and the consequent use of GIS platforms for reconstruction of archaeological landscapes based on remotely sensed data. Feature extraction of multispectral remotely sensing image is an important task before any further processing. High resolution remote sensing data, especially panchromatic, is an important input for the analysis of various types of image characteristics; it plays an important role in the visual systems for recognition and interpretation of given data. The methods proposed rely on an object-oriented approach based on a theory for the analysis of spatial structures called mathematical morphology. The term "morphology" stems from the fact that it aims at analysing object shapes and forms. It is mathematical in the sense that the analysis is based on the set theory, integral geometry, and lattice algebra. Mathematical morphology has proven to be a powerful image analysis technique; two-dimensional grey tone images are seen as three-dimensional sets by associating each image pixel with an elevation proportional to its intensity level. An object of known shape and size, called the structuring element, is then used to investigate the morphology of the input set. This is achieved by positioning the origin of the structuring element to every possible position of the space and testing, for each position, whether the structuring element either is included or has a nonempty intersection with the studied set. The shape and size of the structuring element must be selected according to the morphology of the searched image structures. Other two feature extraction techniques were used, eCognition and ENVI module SW, in order to compare the results. These techniques were

  9. Buried target detection in FLIR images using Shearlet features

    NASA Astrophysics Data System (ADS)

    Tuomanen, Brian; Stone, Kevin; Madison, Timothy; Popescu, Mihail; Keller, James

    2013-06-01

    In this paper we investigate a new approach for representing objects in FLIR images based on shearlets. Similar to wavelets, shearlets represent an affine system for image representation obtained by scaling and translation of a generating function called mother shearlet. Unlike wavelets, the mother shearlet has an extra parameter called shear that allows the shearlet transform to be anisotropic. Anisotropic property of the shearlet transform could allow for a better representation of objects with irregular shape. We test our representation methodology on Froward looking long wave infrared (LWIR) images obtained from an IR camera installed on a moving vehicle. Objects of interest (spots) are detected in each frame using a prescreener presented in our previous work. Each spot is then represented using its shearlet features and assigned a confidence coming from a support vector machine classifier. We compare shearlets to various traditional features such as local binary patterns (LPB) and histogram of gradients (HOG). The comparison is performed on a large dataset that consists of 16 runs at a US Army test site.

  10. 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

  11. Multimodal Ultrawide-Field Imaging Features in Waardenburg Syndrome.

    PubMed

    Choudhry, Netan; Rao, Rajesh C

    2015-06-01

    A 45-year-old woman was referred for bilateral irregular fundus pigmentation. Dilated fundus examination revealed irregular hypopigmentation posterior to the equator in both eyes, confirmed by fundus autofluorescence. A thickened choroid was seen on enhanced-depth imaging spectral-domain optical coherence tomography (EDI SD-OCT). Systemic evaluation revealed sensorineural deafness, telecanthus, and a white forelock. Further investigation revealed a first-degree relative with Waardenburg syndrome. Waardenburg syndrome is characterized by a group of features including telecanthus, a broad nasal root, synophrys of the eyebrows, piedbaldism, heterochromia irides, and deafness. Choroidal hypopigmentation is a unique feature that can be visualized with ultrawide-field fundus autofluorescence. The choroid may also be thickened and its thickness measured with EDI SD-OCT. PMID:26114849

  12. Random projections based feature-specific structured imaging.

    PubMed

    Baheti, Pawan K; Neifeld, Mark A

    2008-02-01

    We present a feature-specific imaging system based on the use of structured illumination. The measurements are defined as inner products between the illumination patterns and the object reflectance function, measured on a single photodetector. The illumination patterns are defined using random binary patterns and thus do not employ prior knowledge about the object. Object estimates are generated using L(1)-norm minimization and gradient-projection sparse reconstruction algorithms. The experimental reconstructions show the feasibility of the proposed approach by using 42% fewer measurements than the object dimensionality. PMID:18542256

  13. 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.

  14. 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

  15. Dermatofibroma: Atypical Presentations

    PubMed Central

    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

  16. Line drawing extraction from gray level images by feature integration

    NASA Astrophysics Data System (ADS)

    Yoo, Hoi J.; Crevier, Daniel; Lepage, Richard; Myler, Harley R.

    1994-10-01

    We describe procedures that extract line drawings from digitized gray level images, without use of domain knowledge, by modeling preattentive and perceptual organization functions of the human visual system. First, edge points are identified by standard low-level processing, based on the Canny edge operator. Edge points are then linked into single-pixel thick straight- line segments and circular arcs: this operation serves to both filter out isolated and highly irregular segments, and to lump the remaining points into a smaller number of structures for manipulation by later stages of processing. The next stages consist in linking the segments into a set of closed boundaries, which is the system's definition of a line drawing. According to the principles of Gestalt psychology, closure allows us to organize the world by filling in the gaps in a visual stimulation so as to perceive whole objects instead of disjoint parts. To achieve such closure, the system selects particular features or combinations of features by methods akin to those of preattentive processing in humans: features include gaps, pairs of straight or curved parallel lines, L- and T-junctions, pairs of symmetrical lines, and the orientation and length of single lines. These preattentive features are grouped into higher-level structures according to the principles of proximity, similarity, closure, symmetry, and feature conjunction. Achieving closure may require supplying missing segments linking contour concavities. Choices are made between competing structures on the basis of their overall compliance with the principles of closure and symmetry. Results include clean line drawings of curvilinear manufactured objects. The procedures described are part of a system called VITREO (viewpoint-independent 3-D recognition and extraction of objects).

  17. Iterative feature refinement for accurate undersampled MR image reconstruction.

    PubMed

    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. PMID:27032527

  18. 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.

  19. Imaging Features of Adult Choledochal Cysts: a Pictorial Review

    PubMed Central

    Park, Seong Jin; Yi, Bum Ha; Lee, A Leum; Moon, Jong Ho; Chang, Yun Woo

    2009-01-01

    Choledochal cysts are rare congenital anomalies which are principally diagnosed by disproportional dilatation of the extrahepatic bile ducts. In addition, choledochal cysts are believed to arise from the anomalous union of the common bile duct and pancreatic duct outside the duodenal wall which is also proximal to the sphincter of the Oddi mechanism. The various types of choledochal cysts have been classified on the basis of these anomalous unions (Komi classification) and their anatomical locations (Todani classification). The multidetector computed tomography with reformatted imaging, magnetic resonance cholangiopancreatography, and an endoscopic retrograde cholangiography represent the important techniques providing the anatomical resolution and detail required to properly diagnose and classify choledochal cysts and their associated abnormal features of the biliary tree, as well as their pancreaticobile duct union. This study describes the various imaging features of a choledochal cyst in adults according to the various types of anomalous unions of the pancreaticobile duct according to Komi's classification and anatomic location according to Todani's classification. Lastly, we also review and discuss the associated abnormal findings developed in biliary systems. PMID:19182506

  20. Atypical Cogan's syndrome mimicking encephalitis.

    PubMed

    Lepur, Dragan; Vranjican, Zoran; Himbele, Josip; Barsić, Bruno; Klinar, Igor

    2004-01-01

    Cogan's syndrome is a rare autoimmune multisystem disease. The main clinical features of typical Cogan's syndrome are vestibuloauditory dysfunction and interstitial keratitis. The authors present a case of atypical Cogan's syndrome with headache, fever, deafness, trigeminal neuralgia and electroencephalographic abnormality which mimicked viral encephalitis. PMID:15307593

  1. Motor features in posterior cortical atrophy and their imaging correlates.

    PubMed

    Ryan, Natalie S; Shakespeare, Timothy J; Lehmann, Manja; Keihaninejad, Shiva; Nicholas, Jennifer M; Leung, Kelvin K; Fox, Nick C; Crutch, Sebastian J

    2014-12-01

    Posterior cortical atrophy (PCA) is a neurodegenerative syndrome characterized by impaired higher visual processing skills; however, motor features more commonly associated with corticobasal syndrome may also occur. We investigated the frequency and clinical characteristics of motor features in 44 PCA patients and, with 30 controls, conducted voxel-based morphometry, cortical thickness, and subcortical volumetric analyses of their magnetic resonance imaging. Prominent limb rigidity was used to define a PCA-motor subgroup. A total of 30% (13) had PCA-motor; all demonstrating asymmetrical left upper limb rigidity. Limb apraxia was more frequent and asymmetrical in PCA-motor, as was myoclonus. Tremor and alien limb phenomena only occurred in this subgroup. The subgroups did not differ in neuropsychological test performance or apolipoprotein E4 allele frequency. Greater asymmetry of atrophy occurred in PCA-motor, particularly involving right frontoparietal and peri-rolandic cortices, putamen, and thalamus. The 9 patients (including 4 PCA-motor) with pathology or cerebrospinal fluid all showed evidence of Alzheimer's disease. Our data suggest that PCA patients with motor features have greater atrophy of contralateral sensorimotor areas but are still likely to have underlying Alzheimer's disease. PMID:25086839

  2. 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

  3. Atypical β-Catenin Activated Child Hepatocellular Tumor

    PubMed Central

    Unlu, Havva Akmaz; Karakus, Esra; Yazal Erdem, Arzu; Yakut, Zeynep Ilerisoy

    2015-01-01

    Hepatocellular adenomas are a benign, focal, hepatic neoplasm that have been divided into four subtypes according to the genetic and pathological features. The β-catenin activated subtype accounts for 10-15% of all hepatocellular adenomas and specific magnetic resonance imaging features have been defined for different hepatocellular adenomas subtypes. The current study aimed to report the magnetic resonance imaging features of a well differentiated hepatocellular carcinoma that developed on the basis of β-catenin activated hepatocellular adenomas in a child. In this case, atypical diffuse steatosis was determined in the lesion. In the literature, diffuse steatosis, which is defined as a feature of the hepatocyte nuclear factor-1α-inactivated hepatocellular adenomas subtype, has not been previously reported in any β-catenin activated hepatocellular adenomas case. Interlacing magnetic resonance imaging findings between subtypes show that there are still many mysteries about this topic and larger studies are warranted. PMID:26157702

  4. Atypical β-Catenin Activated Child Hepatocellular Tumor.

    PubMed

    Turan, Aynur; Unlu, Havva Akmaz; Karakus, Esra; Yazal Erdem, Arzu; Yakut, Zeynep Ilerisoy

    2015-06-01

    Hepatocellular adenomas are a benign, focal, hepatic neoplasm that have been divided into four subtypes according to the genetic and pathological features. The β-catenin activated subtype accounts for 10-15% of all hepatocellular adenomas and specific magnetic resonance imaging features have been defined for different hepatocellular adenomas subtypes. The current study aimed to report the magnetic resonance imaging features of a well differentiated hepatocellular carcinoma that developed on the basis of β-catenin activated hepatocellular adenomas in a child. In this case, atypical diffuse steatosis was determined in the lesion. In the literature, diffuse steatosis, which is defined as a feature of the hepatocyte nuclear factor-1α-inactivated hepatocellular adenomas subtype, has not been previously reported in any β-catenin activated hepatocellular adenomas case. Interlacing magnetic resonance imaging findings between subtypes show that there are still many mysteries about this topic and larger studies are warranted. PMID:26157702

  5. Testing atypical depression definitions.

    PubMed

    Benazzi, Franco

    2005-01-01

    The evidence supporting the DSM-IV definition of atypical depression (AD) is weak. This study aimed to test different definitions of AD. Major depressive disorder (MDD) patients (N = 254) and bipolar-II (BP-II) outpatients (N = 348) were interviewed consecutively, during major depressive episodes, with the Structured Clinical Interview for DSM-IV. DSM-IV criteria for AD were followed. AD validators were female gender, young onset, BP-II, axis I comorbidity, bipolar family history. Frequency of DSM-IV AD was 43.0%. AD, versus non-AD, was significantly associated with all AD validators, apart from comorbidity when controlling for age and sex. Factor analysis of atypical symptoms found factor 1 including oversleeping, overeating and weight gain (leaden paralysis at trend correlation), and factor 2 including interpersonal sensitivity, mood reactivity, and leaden paralysis. Multiple logistic regression of factor 1 versus AD validators found significant associations with several validators (including bipolar family history), whereas factor 2 had no significant associations. Findings may support a new definition of AD based on the state-dependent features oversleeping and overeating (plus perhaps leaden paralysis) versus the current AD definition based on a combination of state and trait features. Pharmacological studies are required to support any new definition of AD, as the current concept of AD is based on different response to TCA antidepressants versus non-AD. PMID:16175877

  6. 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.

  7. 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

  8. 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.

  9. Malignant transformation in monostotic fibrous dysplasia: clinical features, imaging features, outcomes in 10 patients, and review.

    PubMed

    Qu, Nan; Yao, Weiwu; Cui, Xiaojiang; Zhang, Huizhen

    2015-01-01

    Malignant transformation in fibrous dysplasia (FD) is uncommon. The purpose of this study was to investigate clinical and imaging features, and outcomes of malignant transformation in monostotic FD.Data for 10 pathologically confirmed malignant transformations in monostotic FD from January 2005 to December 2013 were retrospectively reviewed. Patient data were recorded, and radiographs (n = 10), computed tomography (CT) (n = 5), magnetic resonance (MR) (n = 4), and bone scintigrams (n = 10) were evaluated for lesion location, margin, cortical destruction, marrow involvement, periosteal reaction, and soft tissue mass by 2 musculoskeletal radiologists with agreement by consensus. Clinical features, management, and prognosis were also analyzed for each of the 10 cases.There were 8 male and 2 female patients (mean age 46.5 ± 15.9 years). The affected sites were the femur (n = 4), humerus (n = 2), tibia (n = 3), and ilium (n = 1). Five cases had received previous surgery and 5 cases had no history of surgery. No patients had been given prior irradiation treatment. For the 5 cases with surgery, radiographs and CT showed purely osteolytic lesions with poor margination in the curettage area (n = 5), cortical destruction (n = 5), obvious soft tissue mass (n = 1), and mineralization (n = 2). For the 5 cases without surgery, radiographs and CT identified poorly marginated, osteolytic lesions within or near the area with "ground-glass" opacity (n = 4), cortical erosion (n = 4), and mineralization (n = 2). Magnetic resonance imaging (MRI) also identified lesions with heterogeneous signal intensity and pronounced enhancement. Bone scintigraphy revealed eccentric increased uptake of radionuclide in monostotic lesion (n = 10). Pathology reports revealed osteosarcoma (n = 7), fibrosarcoma (n = 2), and malignant fibrous histiocytoma (MFH) (n = 1). At the end of the study, 1 patient died from tumors, 1

  10. Cassini Imaging of Noncircular Features in Saturn's Rings

    NASA Astrophysics Data System (ADS)

    Spitale, J. N.; Porco, C. C.; Baker, E.; Tiscareno, M.; Burns, J. A.

    2005-08-01

    We report on our initial examination of some of the eccentric features in Saturn's rings observed in Cassini imaging sequences. High-resolution movies and 360-degree azimuthal imaging scans, with radial spatial scales as fine as a few km and longitudinal resolutions as fine as a fraction of a degree, reveal the shapes of these features in great detail. The outer edges of the B ring, anchored by the strongest resonance in Saturn's rings -- the Mimas 2:1 inner Lindblad resonance (ILR) -- shows high-frequency radial departures from a simple m=2 shape, as previously found in Voyager data [1] but now seen with greater precision. The outer edge of the A ring, controlled by the Janus/Epimetheus 7:6 ILR, also appears to depart from the simple m=7 sinusoidal shape expected for this model [1]. High- spatial-frequency azimuthal variability has also been seen in other ring edges throughout the rings and in greater detail in the known narrow eccentric ringlets inhabiting the gaps in Saturn's rings, such as the Huygens gap exterior to the B ring and the Maxwell gap in the C ring, and in newly discovered, more tenuous rings seen in these gaps. The shape of the F-ring core is largely consistent with that determined by Bosh et al. [2], though at fine scales it is highly time-variable (see Chavez et al. and Charnoz et al., this conference). [1] Porco et al., 1984; Icarus 60, 17. [2] Bosh et al., 2002; Icarus 157, 57.

  11. 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. PMID:25966474

  12. Registration of 3-D images using weighted geometrical features

    SciTech Connect

    Maurer, C.R. Jr.; Aboutanos, G.B.; Dawant, B.M.; Maciunas, R.J.; Fitzpatrick, J.M.

    1996-12-01

    In this paper, the authors present a weighted geometrical features (WGF) registration algorithm. Its efficacy is demonstrated by combining points and a surface. The technique is an extension of Besl and McKay`s iterative closest point (ICP) algorithm. The authors use the WGF algorithm to register X-ray computed tomography (CT) and T2-weighted magnetic resonance (MR) volume head images acquired from eleven patients that underwent craniotomies in a neurosurgical clinical trial. Each patient had five external markers attached to transcutaneous posts screwed into the outer table of the skull. The authors define registration error as the distance between positions of corresponding markers that are not used for registration. The CT and MR images are registered using fiducial points (marker positions) only, a surface only, and various weighted combinations of points and a surface. The CT surface is derived from contours corresponding to the inner surface of the skull. The MR surface is derived from contours corresponding to the cerebrospinal fluid (CSF)-dura interface. Registration using points and a surface is found to be significantly more accurate than registration using only points or a surface.

  13. Automated Image Retrieval of Chest CT Images Based on Local Grey Scale Invariant Features.

    PubMed

    Arrais Porto, Marcelo; Cordeiro d'Ornellas, Marcos

    2015-01-01

    Textual-based tools are regularly employed to retrieve medical images for reading and interpretation using current retrieval Picture Archiving and Communication Systems (PACS) but pose some drawbacks. All-purpose content-based image retrieval (CBIR) systems are limited when dealing with medical images and do not fit well into PACS workflow and clinical practice. This paper presents an automated image retrieval approach for chest CT images based local grey scale invariant features from a local database. Performance was measured in terms of precision and recall, average retrieval precision (ARP), and average retrieval rate (ARR). Preliminary results have shown the effectiveness of the proposed approach. The prototype is also a useful tool for radiology research and education, providing valuable information to the medical and broader healthcare community. PMID:26262345

  14. Relationship between atypical depression and social anxiety disorder.

    PubMed

    Koyuncu, Ahmet; Ertekin, Erhan; Ertekin, Banu Aslantaş; Binbay, Zerrin; Yüksel, Cağrı; Deveci, Erdem; Tükel, Raşit

    2015-01-30

    In this study, we aimed to investigate the effects of atypical and non-atypical depression comorbidity on the clinical characteristics and course of social anxiety disorder (SAD). A total of 247 patients with SAD were enrolled: 145 patients with a current depressive episode (unipolar or bipolar) with atypical features, 43 patients with a current depressive episode with non-atypical features and 25 patients without a lifetime history of depressive episodes were compared regarding sociodemographic and clinical features, comorbidity rates, and severity of SAD, depression and functional impairment. Thirty four patients with a past but not current history of major depressive episodes were excluded from the comparisons. 77.1% of current depressive episodes were associated with atypical features. Age at onset of SAD and age at initial major depressive episode were lower in the group with atypical depression than in the group with non-atypical depression. History of suicide attempts and bipolar disorder comorbidity was more common in the atypical depression group as well. Atypical depression group has higher SAD and depression severity and lower functionality than group with non-atypical depression. Our results indicate that the presence of atypical depression is associated with more severe symptoms and more impairment in functioning in patients with SAD. PMID:25454116

  15. 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

  16. Atypical Presentations of Molar Pregnancy: Diagnostic Roles of Imaging, β-Human Chorionic Gonadotropin Measurement, and p57 Immunostaining.

    PubMed

    Mohamed, Sara A; Al-Hendy, Ayman; Ghamande, Sharad; Chaffin, Joanna; Browne, Paul

    2016-03-01

    In modern practice , the diagnosis of molar pregnancy is made at an early gestational age. The opportunity to diagnose gestational trophoblastic disease (GTD) using sonography alone occurs less frequently. The classic appearance of a "snowstorm" in the endometrial cavity and bilateral theca lutein cysts still applies to the diagnosis of second-trimester GTD. The diagnosis of first-trimester GTD requires increased clinical suspicion. If the sonographic appearance of the pregnancy is atypical, GTD should be included in the differential diagnosis. Additional nonimaging criteria such as serial quantitative β-human chorionic gonadotropin levels, pathologic examination, and p57 (cyclin-dependent kinase inhibitor 1C protein) immunostaining can accurately confirm the diagnosis of GTD. PMID:26860483

  17. Combined optimization of image-gathering and image-processing systems for scene feature detection

    NASA Technical Reports Server (NTRS)

    Halyo, Nesim; Arduini, Robert F.; Samms, Richard W.

    1987-01-01

    The relationship between the image gathering and image processing systems for minimum mean squared error estimation of scene characteristics is investigated. A stochastic optimization problem is formulated where the objective is to determine a spatial characteristic of the scene rather than a feature of the already blurred, sampled and noisy image data. An analytical solution for the optimal characteristic image processor is developed. The Wiener filter for the sampled image case is obtained as a special case, where the desired characteristic is scene restoration. Optimal edge detection is investigated using the Laplacian operator x G as the desired characteristic, where G is a two dimensional Gaussian distribution function. It is shown that the optimal edge detector compensates for the blurring introduced by the image gathering optics, and notably, that it is not circularly symmetric. The lack of circular symmetry is largely due to the geometric effects of the sampling lattice used in image acquisition. The optimal image gathering optical transfer function is also investigated and the results of a sensitivity analysis are shown.

  18. W-transform method for feature-oriented multiresolution image retrieval

    SciTech Connect

    Kwong, M.K.; Lin, B.

    1995-07-01

    Image database management is important in the development of multimedia technology. Since an enormous amount of digital images is likely to be generated within the next few decades in order to integrate computers, television, VCR, cables, telephone and various imaging devices. Effective image indexing and retrieval systems are urgently needed so that images can be easily organized, searched, transmitted, and presented. Here, the authors present a local-feature-oriented image indexing and retrieval method based on Kwong, and Tang`s W-transform. Multiresolution histogram comparison is an effective method for content-based image indexing and retrieval. However, most recent approaches perform multiresolution analysis for whole images but do not exploit the local features present in the images. Since W-transform is featured by its ability to handle images of arbitrary size, with no periodicity assumptions, it provides a natural tool for analyzing local image features and building indexing systems based on such features. In this approach, the histograms of the local features of images are used in the indexing, system. The system not only can retrieve images that are similar or identical to the query images but also can retrieve images that contain features specified in the query images, even if the retrieved images as a whole might be very different from the query images. The local-feature-oriented method also provides a speed advantage over the global multiresolution histogram comparison method. The feature-oriented approach is expected to be applicable in managing large-scale image systems such as video databases and medical image databases.

  19. 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.

  20. 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

  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. PMID:26552069

  2. Postmortem imaging: MDCT features of postmortem change and decomposition.

    PubMed

    Levy, Angela D; Harcke, Howard Theodore; Mallak, Craig T

    2010-03-01

    Multidetector computed tomography (MDCT) has emerged as an effective imaging technique to augment forensic autopsy. Postmortem change and decomposition are always present at autopsy and on postmortem MDCT because they begin to occur immediately upon death. Consequently, postmortem change and decomposition on postmortem MDCT should be recognized and not mistaken for a pathologic process or injury. Livor mortis increases the attenuation of vasculature and dependent tissues on MDCT. It may also produce a hematocrit effect with fluid levels in the large caliber blood vessels and cardiac chambers from dependent layering erythrocytes. Rigor mortis and algor mortis have no specific MDCT features. In contrast, decomposition through autolysis, putrefaction, and insect and animal predation produce dramatic alterations in the appearance of the body on MDCT. Autolysis alters the attenuation of organs. The most dramatic autolytic changes on MDCT are seen in the brain where cerebral sulci and ventricles are effaced and gray-white matter differentiation is lost almost immediately after death. Putrefaction produces a pattern of gas that begins with intravascular gas and proceeds to gaseous distension of all anatomic spaces, organs, and soft tissues. Knowledge of the spectrum of postmortem change and decomposition is an important component of postmortem MDCT interpretation. PMID:20010292

  3. 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

  4. 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.

  5. 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

  6. 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.…

  7. Semantic interpretation of robust imaging features for Fuhrman grading of renal carcinoma

    PubMed Central

    Champion, Andrew; Lu, Guolan; Walker, Marcus; Kothari, Sonal; Osunkoya, Adeboye O.; Wang, May D.

    2016-01-01

    Pattern recognition in tissue biopsy images can assist in clinical diagnosis and identify relevant image characteristics linked with various biological characteristics. Although previous work suggests several informative imaging features for pattern recognition, there exists a semantic gap between characteristics of these features and pathologists’ interpretation of histopathological images. To address this challenge, we develop a clinical decision support system for automated Fuhrman grading of renal carcinoma biopsy images. We extract 1316 color, shape, texture and topology features and develop one vs. all models for four Fuhrman grades. Our models are highly accurate with 90.4% accuracy in a four-class prediction. Predictivity analysis suggests good generalization of the model development methodology through robustness to dataset sampling in cross-validation. We provide a semantic interpretation for the imaging features used in these models by linking features to pathologists’ grading criteria. Our study identifies novel imaging features that are semantically linked to Fuhrman grading criteria. PMID:25571472

  8. 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.

  9. Important clinical features of atypical antipsychotics in acute bipolar depression that inform routine clinical care: a review of pivotal studies with number needed to treat.

    PubMed

    Gao, Keming; Yuan, Chengmei; Wu, Renrong; Chen, Jun; Wang, Zuowei; Fang, Yiru; Calabrese, Joseph R

    2015-10-01

    English-language literature cited in MEDLINE from January, 1980 to October 30, 2014 was searched by using terms of antipsychotic, generic and brand names of atypical antipsychotics, "bipolar depression/bipolar disorder", "placebo", and "trial". The parameters of response (≥50% improvement on MADRS, Montgomery-Asberg Depression Rating Scale total score), remission (either ≤12 or 8 on MADRS total score at endpoint), discontinuation due to adverse events (DAEs), somnolence, ≥7% weight gain, overall extrapyramidal side-effects (EPSs), and akathisia, were extracted from originally published primary outcome papers. The number needed to treat to benefit (NNT) for response and remission or harm (NNH) for DAEs or other side effects relative to placebo were estimated and presented with the estimate and 95% confidence interval. Olanzapine monotherapy, olanzapine-fluoxetine combination (OFC), quetiapine-IR monotherapy, quetiapine-XR monotherapy, lurasidone monotherapy, and lurasidone adjunctive therapy were superior to placebo with NNTs for responses of 11-12, 4, 7-8, 4, 4-5, and 7, and NNTs for remission of 11-12, 4, 5-11, 7, 6-7, and 6, respectively. There was no significant difference between OFC and lamotrigine, and between aripiprazole or ziprasidone and placebo in response and remission. Olanzapine monotherapy, quetiapine-IR, quetiapine-XR, aripiprazole, and ziprasidone 120-160 mg/day had significantly increased risk for DAEs with NNHs of 24, 8-14, 9, 12, and 10, respectively. For somnolence, quetiapine-XR had the smallest NNH of 4. For ≥7% weight gain, olanzapine monotherapy and OFC had the smallest NNHs with both of 5. For akathisia, aripiprazole had the smallest NNH of 5. These findings suggest that among the FDA-approved agents including OFC, quetiapine-IR and -XR, lurasidone monotherapy and adjunctive therapy to a mood stabilizer, the differences in the NNTs for response and remission are small, but the differences in NNHs for DAEs and common side

  10. Diabetic mastopathy: imaging features and the role of image-guided biopsy in its diagnosis

    PubMed Central

    2016-01-01

    Purpose: The goal of this study was to evaluate the imaging features of diabetic mastopathy (DMP) and the role of image-guided biopsy in its diagnosis. Methods: Two experienced radiologists retrospectively reviewed the mammographic and sonographic images of 19 pathologically confirmed DMP patients. The techniques and results of the biopsies performed in each patient were also reviewed. Results: Mammograms showed negative findings in 78% of the patients. On ultrasonography (US), 13 lesions were seen as masses and six as non-mass lesions. The US features of the mass lesions were as follows: irregular shape (69%), oval shape (31%), indistinct margin (69%), angular margin (15%), microlobulated margin (8%), well-defined margin (8%), heterogeneous echogenicity (62%), hypoechoic echogenicity (38%), posterior shadowing (92%), parallel orientation (100%), the absence of calcifications (100%), and the absence of vascularity (100%). Based on the US findings, 17 lesions (89%) were classified as Breast Imaging Reporting and Data System category 4 and two (11%) as category 3. US-guided core biopsy was performed in 18 patients, and 10 (56%) were diagnosed with DMP on that basis. An additional vacuum-assisted biopsy was performed in seven patients and all were diagnosed with DMP. Conclusion: The US features of DMP were generally suspicious for malignancy, whereas the mammographic findings were often negative or showed only focal asymmetry. Core biopsy is an adequate method for initial pathological diagnosis. However, since it yields non-diagnostic results in a considerable number of cases, the evaluation of correlations between imaging and pathology plays an important role in the diagnostic process. PMID:26810194

  11. [Image Feature Extraction and Discriminant Analysis of Xinjiang Uygur Medicine Based on Color Histogram].

    PubMed

    Hamit, Murat; Yun, Weikang; Yan, Chuanbo; Kutluk, Abdugheni; Fang, Yang; Alip, Elzat

    2015-06-01

    Image feature extraction is an important part of image processing and it is an important field of research and application of image processing technology. Uygur medicine is one of Chinese traditional medicine and researchers pay more attention to it. But large amounts of Uygur medicine data have not been fully utilized. In this study, we extracted the image color histogram feature of herbal and zooid medicine of Xinjiang Uygur. First, we did preprocessing, including image color enhancement, size normalizition and color space transformation. Then we extracted color histogram feature and analyzed them with statistical method. And finally, we evaluated the classification ability of features by Bayes discriminant analysis. Experimental results showed that high accuracy for Uygur medicine image classification was obtained by using color histogram feature. This study would have a certain help for the content-based medical image retrieval for Xinjiang Uygur medicine. PMID:26485983

  12. Feature Extraction Of Retinal Images Interfaced With A Rule-Based Expert System

    NASA Astrophysics Data System (ADS)

    Ishag, Na seem; Connell, Kevin; Bolton, John

    1988-12-01

    Feature vectors are automatically extracted from a library of digital retinal images after considerable image processing. Main features extracted are location of optic disc, cup-to-disc ratio using Hough transform techniques and histogram and binary enhancement algorithms, and blood vessel locations. These feature vectors are used to form a relational data base of the images. Relational operations are then used to extract pertinent information from the data base to form replies to queries from the rule-based expert system.

  13. 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

  14. Automated Development of Feature Extraction Tools for Planetary Science Image Datasets

    NASA Astrophysics Data System (ADS)

    Plesko, C.; Brumby, S.; Asphaug, E.

    2003-03-01

    We explore development of feature extraction algorithms for Mars Orbiter Camera narrow angle data using GENIE machine learning software. The algorithms are successful at detecting craters within the images, and generalize well to a new image.

  15. Image search engine with selective filtering and feature-element-based classification

    NASA Astrophysics Data System (ADS)

    Li, Qing; Zhang, Yujin; Dai, Shengyang

    2001-12-01

    With the growth of Internet and storage capability in recent years, image has become a widespread information format in World Wide Web. However, it has become increasingly harder to search for images of interest, and effective image search engine for the WWW needs to be developed. We propose in this paper a selective filtering process and a novel approach for image classification based on feature element in the image search engine we developed for the WWW. First a selective filtering process is embedded in a general web crawler to filter out the meaningless images with GIF format. Two parameters that can be obtained easily are used in the filtering process. Our classification approach first extract feature elements from images instead of feature vectors. Compared with feature vectors, feature elements can better capture visual meanings of the image according to subjective perception of human beings. Different from traditional image classification method, our classification approach based on feature element doesn't calculate the distance between two vectors in the feature space, while trying to find associations between feature element and class attribute of the image. Experiments are presented to show the efficiency of the proposed approach.

  16. 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.

  17. 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

  18. 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.

  19. 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)

  20. 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.

  1. Analysis of mammographic microcalcifications using gray-level image structure features

    SciTech Connect

    Dhawan, A.P.; Chitre, Y.; Kaiser-Bonasso, C.; Moskowitz, M.

    1996-06-01

    Most of the techniques used in the computerized analysis of mammographic microcalcifications use shape features on the segmented regions of microcalcifications extracted from the digitized mammograms. Since mammographic images usually suffer from poorly defined microcalcification features, the extraction of shape features based on a segmentation process may not accurately represent microcalcifications. In this paper, the authors define a set of image structure features for classification of malignancy. Two categories of correlated gray-level image structure features are defined for classification of difficult-to-diagnose cases. The first category of features includes second-order histogram statistics-based features representing the global texture and the wavelet decomposition-based features representing the local texture of the microcalcification area of interest. The second category of features represents the first-order gray-level histogram-based statistics of the segmented microcalcification regions and the size, number, and distance features of the segmented microcalcification cluster. Various features in each category were correlated with the biopsy examination results of 191 difficult-to-diagnose cases for selection of the best set of features representing the complete gray-level image structure information. The selection of the best features was performed using the multivariate cluster analysis as well as a genetic algorithm (GA)-based search method. The selected features were used for classification using backpropagation neural network and parametric statistical classifiers. Receiver operating characteristic (ROC) analysis was performed to compare the neural network-based classification with linear and k-nearest neighbor (KNN) classifiers.

  2. 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.

  3. 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.

  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. Wavelet Algorithm for Feature Identification and Image Analysis

    Energy Science and Technology Software Center (ESTSC)

    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)

  6. 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.

  7. Biomedical imaging modality classification using combined visual features and textual terms.

    PubMed

    Han, Xian-Hua; Chen, Yen-Wei

    2011-01-01

    We describe an approach for the automatic modality classification in medical image retrieval task of the 2010 CLEF cross-language image retrieval campaign (ImageCLEF). This paper is focused on the process of feature extraction from medical images and fuses the different extracted visual features and textual feature for modality classification. To extract visual features from the images, we used histogram descriptor of edge, gray, or color intensity and block-based variation as global features and SIFT histogram as local feature. For textual feature of image representation, the binary histogram of some predefined vocabulary words from image captions is used. Then, we combine the different features using normalized kernel functions for SVM classification. Furthermore, for some easy misclassified modality pairs such as CT and MR or PET and NM modalities, a local classifier is used for distinguishing samples in the pair modality to improve performance. The proposed strategy is evaluated with the provided modality dataset by ImageCLEF 2010. PMID:21912534

  8. 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. PMID:26498516

  9. 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. PMID:26216778

  10. Necrotic intramuscular chloroma with infection: magnetic resonance imaging features.

    PubMed

    Yun, Do Young; Im, Soo Ah; Cho, Bin; Park, Gyeong Sin

    2011-12-01

    We recently experienced the case of an intramuscular chloroma with infection in a 7-year-old boy diagnosed with acute myeloid leukemia. Conventional magnetic resonance imaging (MRI) showed that the lesion mimicked an abscess, but diffusion-weighted imaging showed no diffusion restriction. These results suggested that the interior cystic portion was serous. On histopathological findings, a chloroma was diagnosed on the wall of a mass. Culture of the interior fluid revealed that Klebsiella pneumoniae was present. MRI differentiation is difficult even with diffusion-weighted images. PMID:22009427

  11. Imaging features of midface injectable fillers and associated complications.

    PubMed

    Ginat, D T; Schatz, C J

    2013-08-01

    Injectable fillers are increasingly used for midface augmentation, which can be performed for facial rejuvenation and treatment of HIV facial lipoatrophy. A variety of temporary and permanent filler agents has been developed, including calcium hydroxylapatite, collagen, liquid silicone, polytetrafluoroethylene, hyaluronic acid, poly-l-lactic acid, and polyacrylamide gel. Facial fillers are sometimes encountered on radiologic imaging incidentally and should not be mistaken for pathology. Alternatively, patients with facial fillers may undergo imaging specifically to evaluate associated complications, such as infection, overfilling, migration, foreign-body reaction, and scarring. Therefore, it is important to be familiar with the imaging appearances of the various filler materials and their complications. PMID:22837310

  12. Millimeter wave imaging radiometer with optical design features

    NASA Astrophysics Data System (ADS)

    Schuchardt, J. M.; Newton, J. M.; Morton, T. P.

    1981-02-01

    Unique techniques are being used to develop self-contained imaging radiometers operating at single and multiple frequencies near 35, 95, and 183 GHz. This paper describes a radiometric imaging system which makes use of both 35 and 95 GHz receivers, both vertical and horizontal polarizations, an elevation-over-azimuth antenna positioner, highly automated scanning and data acquisition routines, real-time TV display of the scene being scanned, and immediate color display of recorded radiometric images. The RF sections use Rexolite lenses for low loss beam confinement, and low-loss reflective metallic surfaces for both Dicke chopping and calibration beam selection.

  13. Feature point based image watermarking with insertions, deletions, and substitution codes

    NASA Astrophysics Data System (ADS)

    Bardyn, Dieter; Belet, Philippe; Dams, Tim; Dooms, Ann; Schelkens, Peter

    2010-01-01

    In this paper we concentrate on robust image watermarking (i.e. capable of resisting common signal processing operations and intentional attacks to destroy the watermark) based on image features. Kutter et al.7 motivated that well chosen image features survive admissible image distortions and hence can benefit the watermarking process. These image features are used as location references for the region in which the watermark is embedded. To realize the latter, we make use of previous work16 where a ring-shaped region, centered around an image feature is determined for watermark embedding. We propose to choose a specific sequence of image features according to strict criteria so that the image features have large distance to other chosen image features so that the ring shaped embedding regions do not overlap. Nevertheless, such a setup remains prone to insertion, deletion and substitution errors. Therefore we applied a two-step coding scheme similar to the one employed by Coumou and Sharma4 for speech watermarking. Our contribution here lies in extending Coumou and Sharma's one dimensional scheme to the two dimensional setup that is associated with our watermarking technique. The two-step coding scheme concatenates an outer Reed-Solomon error-correction code with an inner, blind, synchronization mechanism.

  14. 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.

  15. 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.

  16. Magnetic Resonance Imaging Features of a Juxtaglomerular Cell Tumor

    PubMed Central

    Kang, Suhai; Guo, Aitao; Wang, Haiyi; Ma, Lu; Xie, Zongyu; Li, Jinglong; Tonge, Xinyuan; Ye, Huiyi

    2015-01-01

    Objective: To retrospectively determine whether magnetic resonance imaging (MRI) findings can help differentiate a juxtaglomerular cell tumor (JCT) from clear cell renal cell carcinoma (ccRCC). Materials and Methods: Eight patients with JCTs and 24 patients with pathologically proven ccRCC were included for image analysis. All patients underwent unenhanced MRI and dynamic contrast-enhanced MRI. Fat-suppressed T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), in- and opposed-phase imaging, and fat-suppressed preliver acquisitions with volume acceleration sequences were performed before enhancement. After the administration of contrast, dynamic imaging was performed in the corticomedullary, nephrographic, and excretory phases. Student's t-test, t′-test, Chi-square test, and nonparametric Kruskal–Wallis H-test were used to determine the significance of the difference between the two groups. The sensitivity and specificity of the MRI findings were calculated. Results: In patients with a JCT, a cystic part of the lesion of <10%, isointensity or mild hyperintensity on T2WI, heterogeneous hyperintensity on DWI, less signal drop (<10%) in in- and opposed-phase imaging, and a degree of enhancement <200% in the corticomedullary phase showed statistically significant differences compared with those of ccRCC (P < 0.05). After combining a lower apparent diffusion coefficient (ADC) value (heterogeneous hyperintensity) on DWI and a degree of enhancement <200% in the corticomedullary phase using a parallel test, the sensitivity and specificity were 90.9% and 91.7%, respectively. Conclusions: Isointensity or mild hyperintensity on T2WI, a lower ADC value (heterogeneous hyperintensity) on DWI, and a degree of enhancement <200% in the corticomedullary phase are the major MRI findings for JCTs, combined with relative clinical manifestations and excluding other renal masses. A main solid tumor, less signal drop (<10%) in in- and opposed-phase imaging, and a less

  17. 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.

  18. 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…

  19. Fibroblastic Type Osteosarcoma of the Ulna: a Case Report of a Tumor in a Rare Location with Atypical Imaging Findings

    PubMed Central

    Joo, Ijin; Chung, Jin-Haeng; Oh, Joo Han; Hong, Sung Hwan; Kang, Heung Sik

    2009-01-01

    The ulna is a rare site of origin for osteosarcoma, and purely osteolytic osteosarcomas are uncommonly noted on conventional radiographs. We present a patient with a lytic lesion of the distal ulna for which imaging findings suggested an aneurysmal bone cyst. The lesion was histologically confirmed to be a fibroblastic osteosarcoma. PMID:19182508

  20. 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…

  1. Infrared image segmentation using HOG feature and kernel extreme learning machine

    NASA Astrophysics Data System (ADS)

    Liang, Ying; Wang, Luping; Zhang, Luping

    2015-10-01

    Image segmentation is an important application in computer vision. Nowadays, image segmentation of infrared image has not gain as much attention as image segmentation of visible light image. But this application is very useful. For example, searching and tracking targets with infrared search and track system (IRST) has been widely used these days due to its special passive mode. So it can be used as a kind of supplementary equipment for radar. Infrared image segmentation can help computers identify backgrounds of the image, and help it automatically adjust the related parameters for the next work, such as targets recognition or targets detection. Our work proposed a new image segmentation method for infrared image using histogram of oriented gradients (HOG) feature and kernel extreme learning machine (kernel ELM). HOG are feature descriptors which can be used in computer vision and image processing for the purpose of object detection. In this paper, we extract HOG feature of infrared image, and use this feature as the basis for classification. After having feature, we use kernel extreme learning machine to do the segmentation. Kernel extreme learning machine has shown many excellent characteristics in classification. By testing our algorithm proposed in our paper, we demonstrated that our algorithm is effective and feasible.

  2. 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

  3. Quantification winter wheat LAI with HJ-1CCD image features over multiple growing seasons

    NASA Astrophysics Data System (ADS)

    Li, Xinchuan; Zhang, Youjing; Luo, Juhua; Jin, Xiuliang; Xu, Ying; Yang, Wenzhi

    2016-02-01

    Remote sensing images are widely used to map leaf area index (LAI) continuously over landscape. The objective of this study is to explore the ideal image features from Chinese HJ-1 A/B CCD images for estimating winter wheat LAI in Beijing. Image features were extracted from such images over four seasons of winter wheat growth, including five vegetation indices (VIs), principal components (PC), tasseled cap transformations (TCT) and texture parameters. The LAI was significantly correlated with the near-infrared reflectance band, five VIs [normalized difference vegetation index, enhanced vegetation index (EVI), modified nonlinear vegetation index (MNLI), optimization of soil-adjusted vegetation index, and ratio vegetation index], the first principal component (PC1) and the second TCT component (TCT2). However, these image features cannot significantly improve the estimation accuracy of winter wheat LAI in conjunction with eight texture measures. To determine the few ideal features with the best estimation accuracy, partial least squares regression (PLSR) and variable importance in projection (VIP) were applied to predict LAI values. Four remote sensing features (TCT2, PC1, MNLI and EVI) were chosen based on VIP values. The result of leave-one-out cross-validation demonstrated that the PLSR model based on these four features produced better result than the ten features' model, throughout the whole growing season. The results of this study suggest that selecting a few ideal image features is sufficient for LAI estimation.

  4. Edge features extraction from 3D laser point cloud based on corresponding images

    NASA Astrophysics Data System (ADS)

    Li, Xin-feng; Zhao, Zi-ming; Xu, Guo-qing; Geng, Yan-long

    2013-09-01

    An extraction method of edge features from 3D laser point cloud based on corresponding images was proposed. After the registration of point cloud and corresponding image, the sub-pixel edge can be extracted from the image using gray moment algorithm. Then project the sub-pixel edge to the point cloud in fitting scan-lines. At last the edge features were achieved by linking the crossing points. The experimental results demonstrate that the method guarantees accurate fine extraction.

  5. 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

  6. 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

  7. 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

  8. Atypical Visual Saliency in Autism Spectrum Disorder Quantified through Model-Based Eye Tracking.

    PubMed

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

    2015-11-01

    The social difficulties that are a hallmark of autism spectrum disorder (ASD) are thought to arise, at least in part, from atypical attention toward stimuli and their features. To investigate this hypothesis comprehensively, we characterized 700 complex natural scene images with a novel three-layered saliency model that incorporated pixel-level (e.g., contrast), object-level (e.g., shape), and semantic-level attributes (e.g., faces) on 5,551 annotated objects. Compared with 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, and 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

  9. 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

  10. A theory of automatic parameter selection for feature extraction with application to feature-based multisensor image registration.

    PubMed

    DelMarco, Stephen P; Tom, Victor; Webb, Helen F

    2007-11-01

    This paper generalizes the previously developed automated edge-detection parameter selection algorithm of Yitzhaky and Peli. We generalize the approach to arbitrary multidimensional, continuous or discrete parameter spaces, and feature spaces. This generalization enables use of the parameter selection approach with more general image features, for use in feature-based multisensor image registration applications. We investigate the problem of selecting a suitable parameter space sampling density in the automated parameter selection algorithm. A real-valued sensitivity measure is developed which characterizes the effect of parameter space sampling on feature set variability. Closed-form solutions of the sensitivity measure for special feature set relationships are derived. We conduct an analysis of the convergence properties of the sensitivity measure as a function of increasing parameter space sampling density. For certain parameter space sampling sequence types, closed-form expressions for the sensitivity measure limit values are presented. We discuss an approach to parameter space sampling density selection which uses the sensitivity measure convergence behavior. We provide numerical results indicating the utility of the sensitivity measure for selecting suitable parameter values. PMID:17990750

  11. Detection and Classification of Cancer from Microscopic Biopsy Images Using Clinically Significant and Biologically Interpretable Features

    PubMed Central

    Kumar, Rajesh; Srivastava, Subodh

    2015-01-01

    A framework for automated detection and classification of cancer from microscopic biopsy images using clinically significant and biologically interpretable features is proposed and examined. The various stages involved in the proposed methodology include enhancement of microscopic images, segmentation of background cells, features extraction, and finally the classification. An appropriate and efficient method is employed in each of the design steps of the proposed framework after making a comparative analysis of commonly used method in each category. For highlighting the details of the tissue and structures, the contrast limited adaptive histogram equalization approach is used. For the segmentation of background cells, k-means segmentation algorithm is used because it performs better in comparison to other commonly used segmentation methods. In feature extraction phase, it is proposed to extract various biologically interpretable and clinically significant shapes as well as morphology based features from the segmented images. These include gray level texture features, color based features, color gray level texture features, Law's Texture Energy based features, Tamura's features, and wavelet features. Finally, the K-nearest neighborhood method is used for classification of images into normal and cancerous categories because it is performing better in comparison to other commonly used methods for this application. The performance of the proposed framework is evaluated using well-known parameters for four fundamental tissues (connective, epithelial, muscular, and nervous) of randomly selected 1000 microscopic biopsy images. PMID:27006938

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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

  17. Research based on the SoPC platform of feature-based image registration

    NASA Astrophysics Data System (ADS)

    Shi, Yue-dong; Wang, Zhi-hui

    2015-12-01

    This paper focuses on the study of implementing feature-based image registration by System on a Programmable Chip (SoPC) hardware platform. We solidify the image registration algorithm on the FPGA chip, in which embedded soft core processor Nios II can speed up the image processing system. In this way, we can make image registration technology get rid of the PC. And, consequently, this kind of technology will be got an extensive use. The experiment result indicates that our system shows stable performance, particularly in terms of matching processing which noise immunity is good. And feature points of images show a reasonable distribution.

  18. 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.

  19. 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

  20. 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

  1. CT and MR Imaging Diagnosis and Staging of Hepatocellular Carcinoma: Part II. Extracellular Agents, Hepatobiliary Agents, and Ancillary Imaging Features

    PubMed Central

    Choi, Jin-Young; Lee, Jeong-Min

    2014-01-01

    Computed tomography (CT) and magnetic resonance (MR) imaging play critical roles in the diagnosis and staging of hepatocellular carcinoma (HCC). The second article of this two-part review discusses basic concepts of diagnosis and staging, reviews the diagnostic performance of CT and MR imaging with extracellular contrast agents and of MR imaging with hepatobiliary contrast agents, and examines in depth the major and ancillary imaging features used in the diagnosis and characterization of HCC. © RSNA, 2014 PMID:25247563

  2. 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.

  3. 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.

  4. Relationship between Hyperuricemia and Haar-Like Features on Tongue Images

    PubMed Central

    Cui, Yan; Liao, Shizhong; Liu, Hongyu; Wang, Wenhua; Yin, Liqun

    2015-01-01

    Objective. To investigate differences in tongue images of subjects with and without hyperuricemia. Materials and Methods. This population-based case-control study was performed in 2012-2013. We collected data from 46 case subjects with hyperuricemia and 46 control subjects, including results of biochemical examinations and tongue images. Symmetrical Haar-like features based on integral images were extracted from tongue images. T-tests were performed to determine the ability of extracted features to distinguish between the case and control groups. We first selected features using the common criterion P < 0.05, then conducted further examination of feature characteristics and feature selection using means and standard deviations of distributions in the case and control groups. Results. A total of 115,683 features were selected using the criterion P < 0.05. The maximum area under the receiver operating characteristic curve (AUC) of these features was 0.877. The sensitivity of the feature with the maximum AUC value was 0.800 and specificity was 0.826 when the Youden index was maximized. Features that performed well were concentrated in the tongue root region. Conclusions. Symmetrical Haar-like features enabled discrimination of subjects with and without hyperuricemia in our sample. The locations of these discriminative features were in agreement with the interpretation of tongue appearance in traditional Chinese and Western medicine. PMID:25961013

  5. 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.

  6. Statistical fractal border features for MRI breast mass images

    NASA Astrophysics Data System (ADS)

    Penn, Alan I.; Bolinger, Lizann; Loew, Murray H.

    1998-06-01

    MRI has been proposed as an alternative method to mammography for detecting and staging breast cancer. Recent studies have shown that architectural features of breast masses may be useful in improving specificity. Since fractal dimension (fd) has been correlated with roughness, and border roughness is an indicator of malignancy, the fd of the mass border is a promising architectural feature for achieving improved specificity. Previous methods of estimating the fd of the mass border have been unreliable because of limited data or overlay restrictive assumptions of the fractal model. We present preliminary results of a statistical approach in which a sample space of fd estimates is generated from a family of self-affine fractal models. The fd of the mass border is then estimated from the statistics of the sample space.

  7. 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

  8. 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. PMID:26574297

  9. A Novel Framework for Extracting Visual Feature-Based Keyword Relationships from an Image Database

    NASA Astrophysics Data System (ADS)

    Katsurai, Marie; Ogawa, Takahiro; Haseyama, Miki

    In this paper, a novel framework for extracting visual feature-based keyword relationships from an image database is proposed. From the characteristic that a set of relevant keywords tends to have common visual features, the keyword relationships in a target image database are extracted by using the following two steps. First, the relationship between each keyword and its corresponding visual features is modeled by using a classifier. This step enables detection of visual features related to each keyword. In the second step, the keyword relationships are extracted from the obtained results. Specifically, in order to measure the relevance between two keywords, the proposed method removes visual features related to one keyword from training images and monitors the performance of the classifier obtained for the other keyword. This measurement is the biggest difference from other conventional methods that focus on only keyword co-occurrences or visual similarities. Results of experiments conducted using an image database showed the effectiveness of the proposed method.

  10. A standardised protocol for texture feature analysis of endoscopic images in gynaecological cancer

    PubMed Central

    Neofytou, Marios S; Tanos, Vasilis; Pattichis, Marios S; Pattichis, Constantinos S; Kyriacou, Efthyvoulos C; Koutsouris, Dimitris D

    2007-01-01

    Background In the development of tissue classification methods, classifiers rely on significant differences between texture features extracted from normal and abnormal regions. Yet, significant differences can arise due to variations in the image acquisition method. For endoscopic imaging of the endometrium, we propose a standardized image acquisition protocol to eliminate significant statistical differences due to variations in: (i) the distance from the tissue (panoramic vs close up), (ii) difference in viewing angles and (iii) color correction. Methods We investigate texture feature variability for a variety of targets encountered in clinical endoscopy. All images were captured at clinically optimum illumination and focus using 720 × 576 pixels and 24 bits color for: (i) a variety of testing targets from a color palette with a known color distribution, (ii) different viewing angles, (iv) two different distances from a calf endometrial and from a chicken cavity. Also, human images from the endometrium were captured and analysed. For texture feature analysis, three different sets were considered: (i) Statistical Features (SF), (ii) Spatial Gray Level Dependence Matrices (SGLDM), and (iii) Gray Level Difference Statistics (GLDS). All images were gamma corrected and the extracted texture feature values were compared against the texture feature values extracted from the uncorrected images. Statistical tests were applied to compare images from different viewing conditions so as to determine any significant differences. Results For the proposed acquisition procedure, results indicate that there is no significant difference in texture features between the panoramic and close up views and between angles. For a calibrated target image, gamma correction provided an acquired image that was a significantly better approximation to the original target image. In turn, this implies that the texture features extracted from the corrected images provided for better approximations

  11. Color-invariant three-dimensional feature descriptor for color-shift-model-based image processing

    NASA Astrophysics Data System (ADS)

    Lim, Joohyun; Paik, Joonki

    2011-11-01

    We present a novel color-invariant depth feature descriptor for color-shift-model (CSM)-based image processing. Color images acquired by a single camera equipped with multiple color-filter aperture (MCA) contain depth-dependent color misalignment. The amount and direction of the misalignment provides object's distance from the camera. The CSM-based image processing, which represents the combined image-acquisition and depth-estimation framework, requires a color-invariant feature descriptor that can convey depth information. For improving depth-estimation performance, color boosting is performed on a color image acquired by the MCA camera, and CSM-based channel-shifting descriptor vectors, or channel-shifting vectors (CSVs), are generated by using the feasibility test. Color-invariant features are also extracted in the luminance image. The proposed color-invariant three-dimensional (3-D) feature descriptor is finally obtained by combining the CSVs and luminance features. We present experimental analysis of the proposed feature descriptor and show that the descriptors are proportional to the depth of an object. The proposed descriptor can be used for feature-based image matching in various applications, including 3-D scene modeling, 3-D object recognition, 3-D video tracking, and multifocusing, to name a few.

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

    PubMed

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

    2016-01-12

    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

  13. 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

  14. 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.

  15. Spinal Dural Arteriovenous Fistula: Imaging Features and Its Mimics

    PubMed Central

    Jeng, Ying; Chen, David Yen-Ting; Hsu, Hui-Ling; Huang, Yen-Lin; Chen, Chi-Jen

    2015-01-01

    Spinal dural arteriovenous fistula (SDAVF) is the most common spinal vascular malformation, however it is still rare and underdiagnosed. Magnetic resonance imaging findings such as spinal cord edema and dilated and tortuous perimedullary veins play a pivotal role in the confirmation of the diagnosis. However, spinal angiography remains the gold standard in the diagnosis of SDAVF. Classic angiographic findings of SDAVF are early filling of radicular veins, delayed venous return, and an extensive network of dilated perimedullary venous plexus. A series of angiograms of SDAVF at different locations along the spinal column, and mimics of serpentine perimedullary venous plexus on MR images, are demonstrated. Thorough knowledge of SDAVF aids correct diagnosis and prevents irreversible complications. PMID:26357504

  16. Spinal Dural Arteriovenous Fistula: Imaging Features and Its Mimics.

    PubMed

    Jeng, Ying; Chen, David Yen-Ting; Hsu, Hui-Ling; Huang, Yen-Lin; Chen, Chi-Jen; Tseng, Ying-Chi

    2015-01-01

    Spinal dural arteriovenous fistula (SDAVF) is the most common spinal vascular malformation, however it is still rare and underdiagnosed. Magnetic resonance imaging findings such as spinal cord edema and dilated and tortuous perimedullary veins play a pivotal role in the confirmation of the diagnosis. However, spinal angiography remains the gold standard in the diagnosis of SDAVF. Classic angiographic findings of SDAVF are early filling of radicular veins, delayed venous return, and an extensive network of dilated perimedullary venous plexus. A series of angiograms of SDAVF at different locations along the spinal column, and mimics of serpentine perimedullary venous plexus on MR images, are demonstrated. Thorough knowledge of SDAVF aids correct diagnosis and prevents irreversible complications. PMID:26357504

  17. 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

  18. 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. PMID:26589504

  19. 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.

  20. Retroperitoneal nodular fasciitis: magnetic resonance imaging (MRI) and pathological features.

    PubMed

    Meduri; Zuiani; Del Frate C; Bazzocchi

    1998-07-01

    A case of pelvic nodular fasciitis, with particular reference to its peculiar radiological and pathological features is described. Only a few cases of pelvic nodular fasciitis are reported in the English literature and at the best of our knowledge, this is the first case of retroperitoneal origin. This report discusses the role of MRI in the characterization of soft tissue masses. No specific MRI findings of nodular fasciitis were identified and MRI doesn't add any contribution to the differential diagnosis between benign and malignant lesions. As a consequence, the histopathological examination is necessary for a definitive diagnosis. PMID:10358366

  1. Compact Representation of High-Dimensional Feature Vectors for Large-Scale Image Recognition and Retrieval.

    PubMed

    Zhang, Yu; Wu, Jianxin; Cai, Jianfei

    2016-05-01

    In large-scale visual recognition and image retrieval tasks, feature vectors, such as Fisher vector (FV) or the vector of locally aggregated descriptors (VLAD), have achieved state-of-the-art results. However, the combination of the large numbers of examples and high-dimensional vectors necessitates dimensionality reduction, in order to reduce its storage and CPU costs to a reasonable range. In spite of the popularity of various feature compression methods, this paper shows that the feature (dimension) selection is a better choice for high-dimensional FV/VLAD than the feature (dimension) compression methods, e.g., product quantization. We show that strong correlation among the feature dimensions in the FV and the VLAD may not exist, which renders feature selection a natural choice. We also show that, many dimensions in FV/VLAD are noise. Throwing them away using feature selection is better than compressing them and useful dimensions altogether using feature compression methods. To choose features, we propose an efficient importance sorting algorithm considering both the supervised and unsupervised cases, for visual recognition and image retrieval, respectively. Combining with the 1-bit quantization, feature selection has achieved both higher accuracy and less computational cost than feature compression methods, such as product quantization, on the FV and the VLAD image representations. PMID:27046897

  2. Appendicitis associated with intestinal malrotation: imaging diagnosis features. Case report.

    PubMed

    Badea, Radu; Al Hajjar, Nadim; Andreica, Vasile; Procopeţ, Bogdan; Caraiani, Cosmin; Tamas-Szora, Attila

    2012-06-01

    Intestinal malrotation is a rare pathological situation consisting of non-rotation or incomplete rotation of the primitive intestine. Due to the abnormal caecal position inflicted by malrotation, diagnosis of acute appendicitis is difficult. Ultrasonography (US) and Computed Tomography (CT) are relevant and complementary imaging techniques for establishing an otherwise elusive diagnosis. We present the case of 54 year old male presenting with nonspecific abdominal complaints in which US (standard and contrast enhanced) and CT scans identified acute appendicitis associated with malrotated caecum and ascending colon, located in the left hipocondrum. PMID:22675720

  3. Prostate cancer multi-feature analysis using trans-rectal ultrasound images.

    PubMed

    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, yielding 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. PMID:16030375

  4. 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.

  5. 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

  6. Nonlinear registration using B-spline feature approximation and image similarity

    NASA Astrophysics Data System (ADS)

    Kim, June-Sic; Kim, Jae Seok; Kim, In Young; Kim, Sun Il

    2001-07-01

    The warping methods are broadly classified into the image-matching method based on similar pixel intensity distribution and the feature-matching method using distinct anatomical feature. Feature based methods may fail to match local variation of two images. However, the method globally matches features well. False matches corresponding to local minima of the underlying energy functions can be obtained through the similarity based methods. To avoid local minima problem, we proposes non-linear deformable registration method utilizing global information of feature matching and the local information of image matching. To define the feature, gray matter and white matter of brain tissue are segmented by Fuzzy C-Mean (FCM) algorithm. B-spline approximation technique is used for feature matching. We use a multi-resolution B-spline approximation method which modifies multilevel B-spline interpolation method. It locally changes the resolution of the control lattice in proportion to the distance between features of two images. Mutual information is used for similarity measure. The deformation fields are locally refined until maximize the similarity. In two 3D T1 weighted MRI test, this method maintained the accuracy by conventional image matching methods without the local minimum problem.

  7. Combining low level features and visual attributes for VHR remote sensing image classification

    NASA Astrophysics Data System (ADS)

    Zhao, Fumin; Sun, Hao; Liu, Shuai; Zhou, Shilin

    2015-12-01

    Semantic classification of very high resolution (VHR) remote sensing images is of great importance for land use or land cover investigation. A large number of approaches exploiting different kinds of low level feature have been proposed in the literature. Engineers are often frustrated by their conclusions and a systematic assessment of various low level features for VHR remote sensing image classification is needed. In this work, we firstly perform an extensive evaluation of eight features including HOG, dense SIFT, SSIM, GIST, Geo color, LBP, Texton and Tiny images for classification of three public available datasets. Secondly, we propose to transfer ground level scene attributes to remote sensing images. Thirdly, we combine both low-level features and mid-level visual attributes to further improve the classification performance. Experimental results demonstrate that i) Dene SIFT and HOG features are more robust than other features for VHR scene image description. ii) Visual attribute competes with a combination of low level features. iii) Multiple feature combination achieves the best performance under different settings.

  8. 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

  9. Learning How to Extract Rotation-Invariant and Scale-Invariant Features from Texture Images

    NASA Astrophysics Data System (ADS)

    Montoya-Zegarra, Javier A.; Papa, João Paulo; Leite, Neucimar J.; da Silva Torres, Ricardo; Falcão, Alexandre

    2008-12-01

    Learning how to extract texture features from noncontrolled environments characterized by distorted images is a still-open task. By using a new rotation-invariant and scale-invariant image descriptor based on steerable pyramid decomposition, and a novel multiclass recognition method based on optimum-path forest, a new texture recognition system is proposed. By combining the discriminating power of our image descriptor and classifier, our system uses small-size feature vectors to characterize texture images without compromising overall classification rates. State-of-the-art recognition results are further presented on the Brodatz data set. High classification rates demonstrate the superiority of the proposed system.

  10. 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

  11. Atypical parkinsonism: diagnosis and treatment.

    PubMed

    Stamelou, Maria; Bhatia, Kailash P

    2015-02-01

    Atypical parkinsonism comprises typically progressive supranuclear palsy, corticobasal degeneration, and mutilple system atrophy, which are distinct pathologic entities; despite ongoing research, their cause and pathophysiology are still unknown, and there are no biomarkers or effective treatments available. The expanding phenotypic spectrum of these disorders as well as the expanding pathologic spectrum of their classic phenotypes makes the early differential diagnosis challenging for the clinician. Here, clinical features and investigations that may help to diagnose these conditions and the existing limited treatment options are discussed. PMID:25432722

  12. Feature-based tracking algorithms for imaging infrared anti-ship missiles

    NASA Astrophysics Data System (ADS)

    Gray, Greer J.; Aouf, Nabil; Richardson, Mark A.; Butters, Brian; Walmsley, Roy; Nicholls, Edgar

    2011-11-01

    This paper investigates feature based tracking algorithms that could be used within models of imaging infrared anti-ship missile seekers in a simulation environment. The algorithms use global shape based object features such as Fourier Descriptors or Hu Moments to track a target in rendered sensor images. A template of the desired target is saved during acquisition, and matching is performed between the template and the features of unknown objects extracted from subsequent sensor images. The centroid of the object that matches the best becomes the seeker aim-point. A seeker using local features, generated by the Scale Invariant Feature Transform, to track objects will also be examined. It discriminates between objects within the sensor images by clustering SIFT features that have neighbouring regions of similar intensity. The cluster of features whose average neighbouring intensity is the closest to a desired target template is chosen as the highest priority cluster. A variable radius distance metric is used to reject features in this cluster that are too far from the seeker's previous aim-point. The new aim-point is calculated as the centroid of the cluster of remaining features. Comparisons of the three algorithms' ability to track a naval vessel deploying countermeasures will be also presented.

  13. 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

  14. 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

    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 clinically

  15. Design of vector quantizer for image compression using self-organizing feature map and surface fitting.

    PubMed

    Laha, Arijit; Pal, Nikhil R; Chanda, Bhabatosh

    2004-10-01

    We propose a new scheme of designing a vector quantizer for image compression. First, a set of codevectors is generated using the self-organizing feature map algorithm. Then, the set of blocks associated with each code vector is modeled by a cubic surface for better perceptual fidelity of the reconstructed images. Mean-removed vectors from a set of training images is used for the construction of a generic codebook. Further, Huffman coding of the indices generated by the encoder and the difference-coded mean values of the blocks are used to achieve better compression ratio. We proposed two indices for quantitative assessment of the psychovisual quality (blocking effect) of the reconstructed image. Our experiments on several training and test images demonstrate that the proposed scheme can produce reconstructed images of good quality while achieving compression at low bit rates. Index Terms-Cubic surface fitting, generic codebook, image compression, self-organizing feature map, vector quantization. PMID:15462140

  16. 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. PMID:25537575

  17. 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.

  18. Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features.

    PubMed

    Xue, Wufeng; Mou, Xuanqin; Zhang, Lei; Bovik, Alan C; Feng, Xiangchu

    2014-11-01

    Blind image quality assessment (BIQA) aims to evaluate the perceptual quality of a distorted image without information regarding its reference image. Existing BIQA models usually predict the image quality by analyzing the image statistics in some transformed domain, e.g., in the discrete cosine transform domain or wavelet domain. Though great progress has been made in recent years, BIQA is still a very challenging task due to the lack of a reference image. Considering that image local contrast features convey important structural information that is closely related to image perceptual quality, we propose a novel BIQA model that utilizes the joint statistics of two types of commonly used local contrast features: 1) the gradient magnitude (GM) map and 2) the Laplacian of Gaussian (LOG) response. We employ an adaptive procedure to jointly normalize the GM and LOG features, and show that the joint statistics of normalized GM and LOG features have desirable properties for the BIQA task. The proposed model is extensively evaluated on three large-scale benchmark databases, and shown to deliver highly competitive performance with state-of-the-art BIQA models, as well as with some well-known full reference image quality assessment models. PMID:25216482

  19. 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

  20. 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.

  1. 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

  2. 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.

  3. A new approach to modeling the influence of image features on fixation selection in scenes

    PubMed Central

    Nuthmann, Antje; Einhäuser, Wolfgang

    2015-01-01

    Which image characteristics predict where people fixate when memorizing natural images? To answer this question, we introduce a new analysis approach that combines a novel scene-patch analysis with generalized linear mixed models (GLMMs). Our method allows for (1) directly describing the relationship between continuous feature value and fixation probability, and (2) assessing each feature's unique contribution to fixation selection. To demonstrate this method, we estimated the relative contribution of various image features to fixation selection: luminance and luminance contrast (low-level features); edge density (a mid-level feature); visual clutter and image segmentation to approximate local object density in the scene (higher-level features). An additional predictor captured the central bias of fixation. The GLMM results revealed that edge density, clutter, and the number of homogenous segments in a patch can independently predict whether image patches are fixated or not. Importantly, neither luminance nor contrast had an independent effect above and beyond what could be accounted for by the other predictors. Since the parcellation of the scene and the selection of features can be tailored to the specific research question, our approach allows for assessing the interplay of various factors relevant for fixation selection in scenes in a powerful and flexible manner. PMID:25752239

  4. A new approach to modeling the influence of image features on fixation selection in scenes.

    PubMed

    Nuthmann, Antje; Einhäuser, Wolfgang

    2015-03-01

    Which image characteristics predict where people fixate when memorizing natural images? To answer this question, we introduce a new analysis approach that combines a novel scene-patch analysis with generalized linear mixed models (GLMMs). Our method allows for (1) directly describing the relationship between continuous feature value and fixation probability, and (2) assessing each feature's unique contribution to fixation selection. To demonstrate this method, we estimated the relative contribution of various image features to fixation selection: luminance and luminance contrast (low-level features); edge density (a mid-level feature); visual clutter and image segmentation to approximate local object density in the scene (higher-level features). An additional predictor captured the central bias of fixation. The GLMM results revealed that edge density, clutter, and the number of homogenous segments in a patch can independently predict whether image patches are fixated or not. Importantly, neither luminance nor contrast had an independent effect above and beyond what could be accounted for by the other predictors. Since the parcellation of the scene and the selection of features can be tailored to the specific research question, our approach allows for assessing the interplay of various factors relevant for fixation selection in scenes in a powerful and flexible manner. PMID:25752239

  5. A new approach of facial features' localization using a morphological operation in still and sequence images

    NASA Astrophysics Data System (ADS)

    Bozed, Kenz Amhmed; Adjei, Osei; Mansour, Ali

    2013-12-01

    Facial features' localization is a crucial step for many systems of face detection and facial expression recognition. It plays an essential role in human face analysis especially in searching for facial features (mouth, nose and eyes) when the face region is included within the image. The fundamental technique used in facial analysis is to detect the face and subsequently the associated salient features. In this paper, a new Algorithm is based on morphological properties of the face region for the extraction of salient features is proposed. A morphological operation is used to locate the pupils of the eyes and estimate the mouth position according to them. The boundaries of the allocated features are computed as a result when the features are allocated. This algorithm is applied to individual images subsequently application to video sequences. The experimental results achieved from this work indicate that the algorithm has been very successful in recognizing different types of facial expressions.

  6. 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

  7. Introduction: feature issue on phantoms for the performance evaluation and validation of optical medical imaging devices.

    PubMed

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

    2012-06-01

    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

  8. 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

  9. 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…

  10. Content-based image retrieval using features extracted from halftoning-based block truncation coding.

    PubMed

    Guo, Jing-Ming; Prasetyo, Heri

    2015-03-01

    This paper presents a technique for content-based image retrieval (CBIR) by exploiting the advantage of low-complexity ordered-dither block truncation coding (ODBTC) for the generation of image content descriptor. In the encoding step, ODBTC compresses an image block into corresponding quantizers and bitmap image. Two image features are proposed to index an image, namely, color co-occurrence feature (CCF) and bit pattern features (BPF), which are generated directly from the ODBTC encoded data streams without performing the decoding process. The CCF and BPF of an image are simply derived from the two ODBTC quantizers and bitmap, respectively, by involving the visual codebook. Experimental results show that the proposed method is superior to the block truncation coding image retrieval systems and the other earlier methods, and thus prove that the ODBTC scheme is not only suited for image compression, because of its simplicity, but also offers a simple and effective descriptor to index images in CBIR system. PMID:25420264

  11. 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.

  12. The Feature Extraction Based on Texture Image Information for Emotion Sensing in Speech

    PubMed Central

    Wang, Kun-Ching

    2014-01-01

    In this paper, we present a novel texture image feature for Emotion Sensing in Speech (ESS). This idea is based on the fact that the texture images carry emotion-related information. The feature extraction is derived from time-frequency representation of spectrogram images. First, we transform the spectrogram as a recognizable image. Next, we use a cubic curve to enhance the image contrast. Then, the texture image information (TII) derived from the spectrogram image can be extracted by using Laws' masks to characterize emotional state. In order to evaluate the effectiveness of the proposed emotion recognition in different languages, we use two open emotional databases including the Berlin Emotional Speech Database (EMO-DB) and eNTERFACE corpus and one self-recorded database (KHUSC-EmoDB), to evaluate the performance cross-corpora. The results of the proposed ESS system are presented using support vector machine (SVM) as a classifier. Experimental results show that the proposed TII-based feature extraction inspired by visual perception can provide significant classification for ESS systems. The two-dimensional (2-D) TII feature can provide the discrimination between different emotions in visual expressions except for the conveyance pitch and formant tracks. In addition, the de-noising in 2-D images can be more easily completed than de-noising in 1-D speech. PMID:25207869

  13. The feature extraction based on texture image information for emotion sensing in speech.

    PubMed

    Wang, Kun-Ching

    2014-01-01

    In this paper, we present a novel texture image feature for Emotion Sensing in Speech (ESS). This idea is based on the fact that the texture images carry emotion-related information. The feature extraction is derived from time-frequency representation of spectrogram images. First, we transform the spectrogram as a recognizable image. Next, we use a cubic curve to enhance the image contrast. Then, the texture image information (TII) derived from the spectrogram image can be extracted by using Laws' masks to characterize emotional state. In order to evaluate the effectiveness of the proposed emotion recognition in different languages, we use two open emotional databases including the Berlin Emotional Speech Database (EMO-DB) and eNTERFACE corpus and one self-recorded database (KHUSC-EmoDB), to evaluate the performance cross-corpora. The results of the proposed ESS system are presented using support vector machine (SVM) as a classifier. Experimental results show that the proposed TII-based feature extraction inspired by visual perception can provide significant classification for ESS systems. The two-dimensional (2-D) TII feature can provide the discrimination between different emotions in visual expressions except for the conveyance pitch and formant tracks. In addition, the de-noising in 2-D images can be more easily completed than de-noising in 1-D speech. PMID:25207869

  14. 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

  15. Research of detecting details and features of infrared polarization imaging experiment

    NASA Astrophysics Data System (ADS)

    Yang, Fan; Liu, Xiao-cheng; Wang, Ji-zhong

    2013-09-01

    Along with modern infrared camouflage technique developed, it is hard to distinguish target and background by using traditional infrared intensity imaging in general because infrared feature of target and background are tending to consistent. To address this issue, a thought that utilizes infrared polarization imaging technique to detect target is proposed in this paper based on analyzing of the principle of infrared polarization imaging. The experiments are carried out for detecting of infrared low-contrast target imaging. Comparing with the infrared intensity images, the average gradient of the infrared polarization image has been improved 155% and the contrast of target and background has been improved 120% in infrared polarization images. The effective experimental data and imaging law between infrared polarization images and infrared intensity images are obtained that, the technology of infrared polarization imaging can detect details of infrared target more clearly than the infrared intensity imaging, and it can obviously increase the contrast between target and background. Therefore, it is more helpful to detecting details and features of target.

  16. Extending the MEDAS Feature Dictionary to Support Access to Radiological Images

    PubMed Central

    Kaufman, Bryan L.; Naeymi-Rad, Frank; Charletta, Dale A.; Kepic, Anna; Trace, David A.; Naeymirad, Shon; Carmony, Lowell; Spigos, Dimitrios; Evens, Martha

    1989-01-01

    This paper discusses a method of adding a library of radiological images to MEDAS (the Medical Emergency Decision Assistance System). This library is interfaced with the MEDAS Feature Dictionary [1, 2], a dictionary containing terminology for MEDAS knowledge bases. The connections between the radiological images and the terms in the dictionary are used in two ways: 1) To retrieve the images with free text queries. 2) To help in the evaluation of radiological findings during the diagnostic cycle of MEDAS. We plan to use this library as a tool for training students and residents in understanding imaging and its role in diagnostics. This will require construction of a control set of images.

  17. Detection of Important Atmospheric and Surface Features by Employing Principal Component Image Transformation of GOES Imagery.

    NASA Astrophysics Data System (ADS)

    Hillger, Donald W.; Ellrod, Gary P.

    2003-05-01

    The detection of dust, fire hot spots, and smoke from the Geostationary Operational Environmental Satellite (GOES) is made easier by employing the principal component image (PCI) technique. PCIs are created by an eigenvector transformation of spectral band images from the five-band GOES Imager. The transformation is a powerful tool that provides a new set of images that are linear combinations of the original spectral band images. This facilitates viewing the explained variance or signal in the available imagery, allowing both gross and more subtle features in the imagery to be seen. Whereas this multispectral technique is normally applied to high-spatial-resolution land remote sensing imagery, the application is herein made to lower-spatial-resolution weather satellite imagery for the purpose of feature detection and enhancement. Features used as examples include atmospheric dust as well as forest and range fire hot spots and their resulting smoke plumes. The applications of PCIs to GOES utilized the three infrared window images (bands 2, 4, and 5) in dust situations as well as the visible image (band 1) in smoke situations. Two conclusions of this study are 1) atmospheric and surface features are more easily identified in multiband PCIs than in the enhanced single-band images or even in some two-band difference images and 2) the elimination of certain bands can be made either directly by inspection of the PCIs, discarding bands that do not to contribute to the PCIs showing the desired features, or by including all available bands and letting the transformation process indicate the bands that are useful for detecting the desired features. This technique will be increasingly useful with the introduction of new and increased numbers of spectral bands with current and future satellite instrumentation.

  18. 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.

  19. Association between atypical parathyroid adenoma and neurofibromatosis.

    PubMed

    Favere, Aline Mesquita Ferreira de; Tsukumo, Daniela Miti; Matos, Patrícia Sabino de; Santos, Sérgio Luiz Marques dos; Lalli, Cristina Alba

    2015-10-01

    Primary hyperparathyroidism is a disease characterized by excessive production of parathyroid hormone (PTH), which is due to a parathyroid adenoma in 85% of cases. An atypical parathyroid adenoma, with some histopathological features of parathyroid carcinoma, may be found in some of the cases, although it may not fulfill all the criteria for this diagnosis. Neurofibromatosis type 1 (NF1) is an autosomal dominant systemic disease that may be associated with hyperparathyroidism. We report here the rare combination of a patient with NF1 and clinical manifestations of hyperparathyroidism due to an atypical parathyroid adenoma. PMID:26421674

  20. 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.

  1. Transform-invariant feature based functional MR image registration and neural activity modelling.

    PubMed

    Gong, Jiaqi; Hao, Qi; Hu, Fei

    2013-01-01

    In this paper, a set of non-rigid image registration and neural activity modelling methods using functional MR Images (fMRI) are proposed based on transform-invariant feature representations. Our work made two contributions. First, we propose to use a transform-invariant feature to improve image registration performance of Iterative Closest Point (ICP) based methods. The proposed feature utilises Gaussian Mixture Models (GMM) to describe the local topological structure of fMRI data. Second, we propose to use a 3-dimensional Scale-Invariant Feature Transform (SIFT) based descriptor to represent neural activities related to drinking behaviour. As a result, neural activities patterns of different subjects drinking water or intaking glucose can be recognised, with strong robustness against various artefacts. PMID:23900434

  2. Scalable Feature Matching by Dual Cascaded Scalar Quantization for Image Retrieval.

    PubMed

    Zhou, Wengang; Yang, Ming; Wang, Xiaoyu; Li, Houqiang; Lin, Yuanqing; Tian, Qi

    2016-01-01

    In this paper, we investigate the problem of scalable visual feature matching in large-scale image search and propose a novel cascaded scalar quantization scheme in dual resolution. We formulate the visual feature matching as a range-based neighbor search problem and approach it by identifying hyper-cubes with a dual-resolution scalar quantization strategy. Specifically, for each dimension of the PCA-transformed feature, scalar quantization is performed at both coarse and fine resolutions. The scalar quantization results at the coarse resolution are cascaded over multiple dimensions to index an image database. The scalar quantization results over multiple dimensions at the fine resolution are concatenated into a binary super-vector and stored into the index list for efficient verification. The proposed cascaded scalar quantization (CSQ) method is free of the costly visual codebook training and thus is independent of any image descriptor training set. The index structure of the CSQ is flexible enough to accommodate new image features and scalable to index large-scale image database. We evaluate our approach on the public benchmark datasets for large-scale image retrieval. Experimental results demonstrate the competitive retrieval performance of the proposed method compared with several recent retrieval algorithms on feature quantization. PMID:26656584

  3. 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

  4. Multi-feature object identification of remote sensing image based on vague soft sets

    NASA Astrophysics Data System (ADS)

    Wei, Bo; Wang, Zhichao; Xie, Qingqing; Zhang, Kailin

    2015-12-01

    Multi-feature classification and image segmentation are two cores in object-oriented classification method of high resolution remote sensing images. Multi-feature object identification is an important part of multi-feature classification, which is identification for the image regions or the segmentation objects segmented by image segmentation under the guidance of a corresponding relationship between objects and features or combination. A method of multi-feature object identification was proposed based on vague soft sets. Firstly, the vague soft sets were formed by building the parameter sets according to spectral characteristics and object-oriented features of the segmentation objects. Secondly, according to general TOPSIS (the Technique for Order Preference by Similarity to Ideal Solution), a TOPSIS based on Vague soft sets for multi-feature object identification was proposed, which obtained a object identification result of the segmentation objects by using similarity measure of vague soft sets to sort attribution of the cover types for the segmentation objects. The experimental results show that the proposed method obtains a correct result of object identification and is feasible and effective.

  5. Identifying quantitative imaging features of posterior fossa syndrome in longitudinal MRI.

    PubMed

    Spiteri, Michaela; Windridge, David; Avula, Shivaram; Kumar, Ram; Lewis, Emma

    2015-10-01

    Up to 25% of children who undergo brain tumor resection surgery in the posterior fossa develop posterior fossa syndrome (PFS). This syndrome is characterized by mutism and disturbance in speech. Our hypothesis is that there is a correlation between PFS and the occurrence of hypertrophic olivary degeneration (HOD) in structures within the posterior fossa, known as the inferior olivary nuclei (ION). HOD is exhibited as an increase in size and intensity of the ION on an MR image. Longitudinal MRI datasets of 28 patients were acquired consisting of pre-, intra-, and postoperative scans. A semiautomated segmentation process was used to segment the ION on each MR image. A full set of imaging features describing the first- and second-order statistics and size of the ION were extracted for each image. Feature selection techniques were used to identify the most relevant features among the MRI features, demographics, and data based on neuroradiological assessment. A support vector machine was used to analyze the discriminative features selected by a generative k-nearest neighbor algorithm. The results indicate the presence of hyperintensity in the left ION as the most diagnostically relevant feature, providing a statistically significant improvement in the classification of patients ([Formula: see text]) when using this feature alone. PMID:26835496

  6. 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.

  7. 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.

  8. 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.

  9. Feature matching method study for uncorrected fish-eye lens image

    NASA Astrophysics Data System (ADS)

    Zhang, Baofeng; Jia, Yanhui; Röning, Juha; Feng, Weijia

    2015-01-01

    Because of the further from the center of image the lower resolution and the severe non-linear distortion are the characteristics of uncorrected fish-eye lens image, the traditional feature matching method can't achieve good performance in the applications of fish-eye lens, which correct distortion firstly and then matches the features in image. Center-symmetric Local Binary Pattern (CS-LBP) is a kind of descriptor based on grayscale information from neighborhood, which has high ability of grayscale invariance and rotation invariance. In this paper, CS-LBP will be combined with Scale Invariant Feature Transform (SIFT) to solve the problem of feature point matching on uncorrected fish-eye image. We first extract the interest points in the pair of fish-eye images by SIFT, and then describe the corresponding regions of the interest points through CS-LBP. Finally the similarity of the regions will be evaluated using the chi-square distance to get the only pair of points. For the specified interest point, the corresponding point in another image can be found out. The experimental results show that the proposed method achieves a satisfying matching performance in uncorrected fish-eye lens image. The study of this article will be useful to enhance the applications of fish-eye lens in the field of 3D reconstruction and panorama restoration.

  10. High-resolution FTIR imaging of colon tissues for elucidation of individual cellular and histopathological features.

    PubMed

    Nallala, Jayakrupakar; Lloyd, Gavin Rhys; Shepherd, Neil; Stone, Nick

    2016-01-21

    Novel technologies that could complement current histopathology based cancer diagnostic methods are under examination. In this endeavour mid-infrared spectroscopic imaging is a promising candidate that can provide valuable bio-molecular information from unstained cells and tissues in a rapid and a non-destructive manner. With this imaging technique, the biochemical information obtained from smaller areas of the tissues can be of clinical significance and hence the measured pixel size. Until recently it was difficult to obtain spectral data from pixels below around 5 microns square. High NA objectives have been utilised to reduce the ideal diffraction limit, enabling for the first time elucidation of subcellular features. In this context, the ability of high-resolution imaging, obtained using novel high-magnification optics retro-fitted onto a bench top FTIR imaging system, to characterise histopathological features in colonic tissues has been tested. Formalin fixed paraffin embedded colon tissues from three different pathologies were imaged directly using the conventional and the high-magnification imaging set-ups. To circumvent chemical de-paraffinization protocols, an extended multiplicative signal correction (EMSC) based electronic de-paraffinization was carried out on all the infrared images. Multivariate analysis of the high-magnification infrared imaging data showed a detailed information of the histological features of the colon tissue in comparison to conventional imaging. Furthermore, high-magnification imaging has enabled a label-free characterization of the mucin rich goblet cell features in an unprecedented manner. The current study demonstrates the applicability of high-magnification FTIR imaging to characterise complex tissues on a smaller scale that could be of clinical significance. PMID:26549223

  11. A major cathepsin B protease from the liver fluke Fasciola hepatica has atypical active site features and a potential role in the digestive tract of newly excysted juvenile parasites.

    PubMed

    Beckham, Simone A; Piedrafita, David; Phillips, Carolyn I; Samarawickrema, Nirma; Law, Ruby H P; Smooker, Peter M; Quinsey, Noelene S; Irving, James A; Greenwood, Deanne; Verhelst, Steven H L; Bogyo, Matthew; Turk, Boris; Coetzer, Theresa H; Wijeyewickrema, Lakshmi C; Spithill, Terry W; Pike, Robert N

    2009-07-01

    The newly excysted juvenile (NEJ) stage of the Fasciola hepatica lifecycle occurs just prior to invasion into the wall of the gut of the host, rendering it an important target for drug development. The cathepsin B enzymes from NEJ flukes have recently been demonstrated to be crucial to invasion and migration by the parasite. Here we characterize one of the cathepsin B enzymes (recombinant FhcatB1) from NEJ flukes. FhcatB1 has biochemical properties distinct from mammalian cathepsin B enzymes, with an atypical preference for Ile over Leu or Arg residues at the P(2) substrate position and an inability to act as an exopeptidase. FhcatB1 was active across a broad pH range (optimal activity at pH 5.5-7.0) and resistant to inhibition by cystatin family inhibitors from sheep and humans, suggesting that this enzyme would be able to function in extracellular environments in its mammalian hosts. It appears, however, that the FhcatB1 protease functions largely as a digestive enzyme in the gut of the parasite, due to the localization of a specific, fluorescently labeled inhibitor with an Ile at the P(2) position. Molecular modelling and dynamics were used to predict the basis for the unusual substrate specificity: a P(2) Ile residue positions the substrate optimally for interaction with catalytic residues of the enzyme, and the enzyme lacks an occluding loop His residue crucial for exopeptidase activity. The unique features of the enzyme, particularly with regard to its specificity and likely importance to a vital stage of the parasite's life cycle, make it an excellent target for therapeutic inhibitors or vaccination. PMID:19401154

  12. Atypical combinations and scientific impact.

    PubMed

    Uzzi, Brian; Mukherjee, Satyam; Stringer, Michael; Jones, Ben

    2013-10-25

    Novelty is an essential feature of creative ideas, yet the building blocks of new ideas are often embodied in existing knowledge. From this perspective, balancing atypical knowledge with conventional knowledge may be critical to the link between innovativeness and impact. Our analysis of 17.9 million papers spanning all scientific fields suggests that science follows a nearly universal pattern: The highest-impact science is primarily grounded in exceptionally conventional combinations of prior work yet simultaneously features an intrusion of unusual combinations. Papers of this type were twice as likely to be highly cited works. Novel combinations of prior work are rare, yet teams are 37.7% more likely than solo authors to insert novel combinations into familiar knowledge domains. PMID:24159044

  13. 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

  14. 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.

  15. 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

  16. Feature evaluation for target/background discrimination in image sequences taken by approaching sensors

    NASA Astrophysics Data System (ADS)

    Schoene, Rene; Meidow, Jochen; Mauer, Edmond

    2010-04-01

    The conspicuity of different targets in image sequences taken by approaching sensors is addressed in applications such as the assessment of camouflage effectiveness or the performance evaluation of autonomous systems. In such evaluation processes the consideration of background characteristics is essential due to the propensity to confuse target and background signatures. Several discriminating features of target and background signature can be derived. Furthermore, the changing aspect and spatial resolution during an approach on a target have to be taken into account. Considering salient points in image sequences, we perform a nominal/actual value comparison by evaluating the receiver operating characteristic (ROC) curve for the detections in each image. Hence, reference regions for targets and backgrounds are provided for the entire image sequence by means of robust image registration. The consideration of the uncertainty for the temporal progression of the ROC curve enables hypothesis testing for well-founded statements about the significance of the determined distinctiveness of targets with respect to their background. The approach is neither restricted to images taken by IR sensors nor applicable to low level image analysis steps only, but can be considered as a general method for the assessment of feature evaluation and target distinctiveness. The analysis method proposed facilitates an objective comparison of object appearance with both, its relevant background and other targets, using different image analysis features. The feasibility and the usefulness of the approach are demonstrated with real data recorded with a FLIR sensor during a field trial on a bare and mock-up target.

  17. A practical salient region feature based 3D multi-modality registration method for medical images

    NASA Astrophysics Data System (ADS)

    Hahn, Dieter A.; Wolz, Gabriele; Sun, Yiyong; Hornegger, Joachim; Sauer, Frank; Kuwert, Torsten; Xu, Chenyang

    2006-03-01

    We present a novel representation of 3D salient region features and its integration into a hybrid rigid-body registration framework. We adopt scale, translation and rotation invariance properties of those intrinsic 3D features to estimate a transform between underlying mono- or multi-modal 3D medical images. Our method combines advantageous aspects of both feature- and intensity-based approaches and consists of three steps: an automatic extraction of a set of 3D salient region features on each image, a robust estimation of correspondences and their sub-pixel accurate refinement with outliers elimination. We propose a region-growing based approach for the extraction of 3D salient region features, a solution to the problem of feature clustering and a reduction of the correspondence search space complexity. Results of the developed algorithm are presented for both mono- and multi-modal intra-patient 3D image pairs (CT, PET and SPECT) that have been acquired for change detection, tumor localization, and time based intra-person studies. The accuracy of the method is clinically evaluated by a medical expert with an approach that measures the distance between a set of selected corresponding points consisting of both anatomical and functional structures or lesion sites. This demonstrates the robustness of the proposed method to image overlap, missing information and artefacts. We conclude by discussing potential medical applications and possibilities for integration into a non-rigid registration framework.

  18. A Composite Approach To The Identification Of High-Level Topological Features In A Histopathologic Image

    NASA Astrophysics Data System (ADS)

    Kuhn, W. P.; Bartels, H. G.; Bartels, P. H.; Richards, D. L.; Saffer, J. S.; Shoemaker, R. L.

    1988-06-01

    Analysis of the large amounts of image data obtainable from very-high-speed scanning laser microscopes places severe demands on computer software and hardware architectures. The automated calculation of features over entire images can provide quantitative data useful to a pathologist who must make a diagnosis. A program that identifies objects of diagnostic interest in an image must utilize a model of the image. An expert system is an effective method for building abstract models of object hierarchies and for utilizing heuristic information. In this paper we discuss a composite approach to image understanding and assessment that utilizes an expert system to control a set of image processing functions for the recognition of various objects in an image.

  19. 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.

  20. 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.

  1. 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

  2. Breast tissue classification in digital tomosynthesis images based on global gradient minimization and texture features

    NASA Astrophysics Data System (ADS)

    Qin, Xulei; Lu, Guolan; Sechopoulos, Ioannis; Fei, Baowei

    2014-03-01

    Digital breast tomosynthesis (DBT) is a pseudo-three-dimensional x-ray imaging modality proposed to decrease the effect of tissue superposition present in mammography, potentially resulting in an increase in clinical performance for the detection and diagnosis of breast cancer. Tissue classification in DBT images can be useful in risk assessment, computer-aided detection and radiation dosimetry, among other aspects. However, classifying breast tissue in DBT is a challenging problem because DBT images include complicated structures, image noise, and out-of-plane artifacts due to limited angular tomographic sampling. In this project, we propose an automatic method to classify fatty and glandular tissue in DBT images. First, the DBT images are pre-processed to enhance the tissue structures and to decrease image noise and artifacts. Second, a global smooth filter based on L0 gradient minimization is applied to eliminate detailed structures and enhance large-scale ones. Third, the similar structure regions are extracted and labeled by fuzzy C-means (FCM) classification. At the same time, the texture features are also calculated. Finally, each region is classified into different tissue types based on both intensity and texture features. The proposed method is validated using five patient DBT images using manual segmentation as the gold standard. The Dice scores and the confusion matrix are utilized to evaluate the classified results. The evaluation results demonstrated the feasibility of the proposed method for classifying breast glandular and fat tissue on DBT images.

  3. 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

  4. Image Analysis, Modeling, Enhancement, Restoration, Feature Extraction and Their Applications in Nondestructive Evaluation and Radio Astronomy.

    NASA Astrophysics Data System (ADS)

    Zheng, Yi.

    The principal topic of this dissertation is the development and application of signal and image processing to Nondestructive Evaluation (NDE) and radio astronomy. The dissertation consists of nine papers published or submitted for publication. Each of them has a specific and unique topic related to signal processing or image processing in NDE or radio astronomy. Those topics are listed in the following. (1) Time series analysis and modeling of Very Large Array (VLA) phase data. (2) Image analysis, feature extraction and various applied enhancement methods for industrial NDE X-ray radiographic images. (3) Enhancing NDE radiographic X-ray images by adaptive regional Kalman filtering. (4) Robotic image segmentation, modeling, and restoration with a rule based expert system. (5) Industrial NDE radiographic X-ray image modeling and Kalman filtering considering signal-dependent colored noise. (6) Computational study of Kalman filtering VLA phase data and its computational performance on a supercomputer. (7) A practical and fast maximum entropy deconvolution method for de-blurring industrial NDE X-ray and infrared images. (8) Local feature enhancement of synthetic radio images by adaptive Kalman filtering. (9) A new technique for correcting phase data of a synthetic -aperture antenna array.

  5. Enhanced Feature Based Mosaicing Technique for Visually and Geometrically Degraded Airborne Synthetic Aperture Radar Images

    NASA Astrophysics Data System (ADS)

    Manikandan, S.; Vardhini, J. P.

    2015-11-01

    In airborne synthetic aperture radar (SAR), there was a major problem encountered in the area of image mosaic in the absence of platform information and sensor information (geocoding), when SAR is applied in large-scale scene and the platform faces large changes. In order to enhance real-time performance and robustness of image mosaic, enhancement based Speeded-Up Robust Features (SURF) mosaic method for airborne SAR is proposed in this paper. SURF is a novel scale-invariant and rotation-invariant feature. It is perfect in its high computation, speed and robustness. In this paper, When the SAR image is acquired, initially the image is enhanced by using local statistic techniques and SURF is applied for SAR image matching accord to its characteristic, and then acquires its invariant feature for matching. In the process of image matching, the nearest neighbor rule for initial matching is used, and the wrong points of the matches are removed through RANSAC fitting algorithm. The proposed algorithm is implemented in different SAR images with difference in scale change, rotation change and noise. The proposed algorithm is compared with other existing algorithms and the quantitative and qualitative measures are calculated and tabulated. The proposed algorithm is robust to changes and the threshold is varied accordingly to increase the matching rate more than 95 %.

  6. 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

  7. 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.

  8. Full-reference quality assessment of stereoscopic images by learning sparse monocular and binocular features

    NASA Astrophysics Data System (ADS)

    Li, Kemeng; Shao, Feng; Jiang, Gangyi; Yu, Mei

    2014-11-01

    Perceptual stereoscopic image quality assessment (SIQA) aims to use computational models to measure the image quality in consistent with human visual perception. In this research, we try to simulate monocular and binocular visual perception, and proposed a monocular-binocular feature fidelity (MBFF) induced index for SIQA. To be more specific, in the training stage, we learn monocular and binocular dictionaries from the training database, so that the latent response properties can be represented as a set of basis vectors. In the quality estimation stage, we compute monocular feature fidelity (MFF) and binocular feature fidelity (BFF) indexes based on the estimated sparse coefficient vectors, and compute global energy response similarity (GERS) index by considering energy changes. The final quality score is obtained by incorporating them together. Experimental results on four public 3D image quality assessment databases demonstrate that in comparison with the most related existing methods, the devised algorithm achieves high consistency alignment with subjective assessment.

  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. 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.

  11. 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

  12. Feature extraction and pattern classification of colorectal polyps in colonoscopic imaging.

    PubMed

    Fu, Jachih J C; Yu, Ya-Wen; Lin, Hong-Mau; Chai, Jyh-Wen; Chen, Clayton Chi-Chang

    2014-06-01

    A computer-aided diagnostic system for colonoscopic imaging has been developed to classify colorectal polyps by type. The modules of the proposed system include image enhancement, feature extraction, feature selection and polyp classification. Three hundred sixty-five images (214 with hyperplastic polyps and 151 with adenomatous polyps) were collected from a branch of a medical center in central Taiwan. The raw images were enhanced by the principal component transform (PCT). The features of texture analysis, spatial domain and spectral domain were extracted from the first component of the PCT. Sequential forward selection (SFS) and sequential floating forward selection (SFFS) were used to select the input feature vectors for classification. Support vector machines (SVMs) were employed to classify the colorectal polyps by type. The classification performance was measured by the Az values of the Receiver Operating Characteristic curve. For all 180 features used as input vectors, the test data set yielded Az values of 88.7%. The Az value was increased by 2.6% (from 88.7% to 91.3%) and 4.4% (from 88.7% to 93.1%) for the features selected by the SFS and the SFFS, respectively. The SFS and the SFFS reduced the dimension of the input vector by 57.2% and 73.8%, respectively. The SFFS outperformed the SFS in both the reduction of the dimension of the feature vector and the classification performance. When the colonoscopic images were visually inspected by experienced physicians, the accuracy of detecting polyps by types was around 85%. The accuracy of the SFFS with the SVM classifier reached 96%. The classification performance of the proposed system outperformed the conventional visual inspection approach. Therefore, the proposed computer-aided system could be used to improve the quality of colorectal polyp diagnosis. PMID:24495469

  13. Chordoid Glioma with Intraventricular Dissemination: A Case Report with Perfusion MR Imaging Features

    PubMed Central

    Ki, So Yeon; Kim, Seul Kee; Heo, Tae Wook; Baek, Byung Hyun; Kim, Hyung Seok

    2016-01-01

    Chordoid glioma is a rare low grade tumor typically located in the third ventricle. Although a chordoid glioma can arise from ventricle with tumor cells having features of ependymal differentiation, intraventricular dissemination has not been reported. Here we report a case of a patient with third ventricular chordoid glioma and intraventricular dissemination in the lateral and fourth ventricles. We described the perfusion MR imaging features of our case different from a previous report. PMID:26798226

  14. Fast computation of rotation-invariant image features by an approximate radial gradient transform.

    PubMed

    Takacs, Gabriel; Chandrasekhar, Vijay; Tsai, Sam S; Chen, David; Grzeszczuk, Radek; Girod, Bernd

    2013-08-01

    We present the radial gradient transform (RGT) and a fast approximation, the approximate RGT (ARGT). We analyze the effects of the approximation on gradient quantization and histogramming. The ARGT is incorporated into the rotation-invariant fast feature (RIFF) algorithm. We demonstrate that, using the ARGT, RIFF extracts features 16× faster than SURF while achieving a similar performance for image matching and retrieval. PMID:23204286

  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. Derivative-based scale invariant image feature detector with error resilience.

    PubMed

    Mainali, Pradip; Lafruit, Gauthier; Tack, Klaas; Van Gool, Luc; Lauwereins, Rudy

    2014-05-01

    We present a novel scale-invariant image feature detection algorithm (D-SIFER) using a newly proposed scale-space optimal 10th-order Gaussian derivative (GDO-10) filter, which reaches the jointly optimal Heisenberg's uncertainty of its impulse response in scale and space simultaneously (i.e., we minimize the maximum of the two moments). The D-SIFER algorithm using this filter leads to an outstanding quality of image feature detection, with a factor of three quality improvement over state-of-the-art scale-invariant feature transform (SIFT) and speeded up robust features (SURF) methods that use the second-order Gaussian derivative filters. To reach low computational complexity, we also present a technique approximating the GDO-10 filters with a fixed-length implementation, which is independent of the scale. The final approximation error remains far below the noise margin, providing constant time, low cost, but nevertheless high-quality feature detection and registration capabilities. D-SIFER is validated on a real-life hyperspectral image registration application, precisely aligning up to hundreds of successive narrowband color images, despite their strong artifacts (blurring, low-light noise) typically occurring in such delicate optical system setups. PMID:24723627

  17. 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

  18. 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.

  19. 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

  20. No-reference hair occlusion assessment for dermoscopy images based on distribution feature.

    PubMed

    Xie, Fengying; Li, Yang; Meng, Rusong; Jiang, Zhiguo

    2015-04-01

    The presence of hair is a common quality problem for dermoscopy images, which may influence the accuracy of lesion analysis. In this paper, a novel no-reference hair occlusion assessment method is proposed according to the distribution feature of hairs in the dermoscopy image. Firstly, the image is adaptively enhanced by simple linear iterative clustering (SLIC) combined with isotropic nonlinear filtering (INF). Then, hairs are extracted from the image by an automatic threshold and meanwhile the postprocessing is used to refine the hair through re-extracting omissive hairs and filtering false hairs. Finally, the degree of hair occlusion is evaluated by an objective metric based on the hair distribution. A series of experiments was carried out on both simulated images and real images. The result shows that the proposed local adaptive hair detection method can work well on both sparse hair and dense hair, and the designed metric can effectively evaluate the degree of hair occlusion. PMID:25701625

  1. 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

  2. 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.

  3. 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

  4. ARIN® procedure for the normalization of multitemporal remote images through vegetative pseudo-invariant features

    NASA Astrophysics Data System (ADS)

    García-Torres, L.; Caballero-Novella, J. J.; Gómez-Candón, D.; Jurado-Expósito, M.

    2013-10-01

    A method was developed to normalize multitemporal remote images based in vegetative pseudo-invariant features (VPIFs), as following: 1) defining the same parcel for each selected VPIF in each multitemporal image; 2) extracting the VIPF spectral bands data for each image; 3) calculating the correction factor (CF) for each image band to fit it to the same expected values, normally for each band the average of the series; 4) obtaining the normalized images by transforming each original image band through the corresponding CF linear functions. We have validated ARIN using a series of six GeoEye-1 satellite images taken over the same Southern of Spain scene, from early April to October. Citrus orchards (CIT), riparian trees (POP), olive orchards (OLI) and Mediterranean forest trees (MFO) were the VPIFs chosen, among others. The VPIFs spectral band correction factors (CFs) to implement the ARIN linear normalization procedure largely varied among spectral bands for any given image and among images for any given spectral band. For the ARIN normalized images, the range and standard deviation of any spectral bands and vegetation indices values were considerably reduced as compared to the original images, regardless the VPIF or the combination of VPIFs selected for normalization, which proves the method efficacy. Moreover, ARIN method was easier and efficient than the absolute calibration QUAC method, and somehow similarly efficient as the highly tunable FLAASH, in which solar position and weather calibration parameters are required. ARIN® software was developed to automatically achieve the previously described procedure.

  5. Feature-specific imaging: Extensions to adaptive object recognition and active illumination based scene reconstruction

    NASA Astrophysics Data System (ADS)

    Baheti, Pawan K.

    Computational imaging (CI) systems are hybrid imagers in which the optical and post-processing sub-systems are jointly optimized to maximize the task-specific performance. In this dissertation we consider a form of CI system that measures the linear projections (i.e., features) of the scene optically, and it is commonly referred to as feature-specific imaging (FSI). Most of the previous work on FSI has been concerned with image reconstruction. Previous FSI techniques have also been non-adaptive and restricted to the use of ambient illumination. We consider two novel extensions of the FSI system in this work. We first present an adaptive feature-specific imaging (AFSI) system and consider its application to a face-recognition task. The proposed system makes use of previous measurements to adapt the projection basis at each step. We present both statistical and information-theoretic adaptation mechanisms for the AFSI system. The sequential hypothesis testing framework is used to determine the number of measurements required for achieving a specified misclassification probability. We demonstrate that AFSI system requires significantly fewer measurements than static-FSI (SFSI) and conventional imaging at low signal-to-noise ratio (SNR). We also show a trade-off, in terms of average detection time, between measurement SNR and adaptation advantage. Experimental results validating the AFSI system are presented. Next we present a FSI system based on the use of structured light. Feature measurements are obtained by projecting spatially structured illumination onto an object and collecting all of the reflected light onto a single photodetector. We refer to this system as feature-specific structured imaging (FSSI). Principal component features are used to define the illumination patterns. The optimal LMMSE operator is used to generate object estimates from the measurements. We demonstrate that this new imaging approach reduces imager complexity and provides improved image

  6. 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

  7. Effectiveness of Global Features for Automatic Medical Image Classification and Retrieval - the experiences of OHSU at ImageCLEFmed.

    PubMed

    Kalpathy-Cramer, Jayashree; Hersh, William

    2008-11-01

    In 2006 and 2007, Oregon Health & Science University (OHSU) participated in the automatic image annotation task for medical images at ImageCLEF, an annual international benchmarking event that is part of the Cross Language Evaluation Forum (CLEF). The goal of the automatic annotation task was to classify 1000 test images based on the Image Retrieval in Medical Applications (IRMA) code, given a set of 10,000 training images. There were 116 distinct classes in 2006 and 2007. We evaluated the efficacy of a variety of primarily global features for this classification task. These included features based on histograms, gray level correlation matrices and the gist technique. A multitude of classifiers including k-nearest neighbors, two-level neural networks, support vector machines, and maximum likelihood classifiers were evaluated. Our official error rates for the 1000 test images were 26% in 2006 using the flat classification structure. The error count in 2007 was 67.8 using the hierarchical classification error computation based on the IRMA code in 2007. Confusion matrices as well as clustering experiments were used to identify visually similar classes. The use of the IRMA code did not help us in the classification task as the semantic hierarchy of the IRMA classes did not correspond well with the hierarchy based on clustering of image features that we used. Our most frequent misclassification errors were along the view axis. Subsequent experiments based on a two-stage classification system decreased our error rate to 19.8% for the 2006 dataset and our error count to 55.4 for the 2007 data. PMID:19884953

  8. 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.

  9. 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

  10. 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

  11. Face detection on distorted images using perceptual quality-aware features

    NASA Astrophysics Data System (ADS)

    Gunasekar, Suriya; Ghosh, Joydeep; Bovik, Alan C.

    2014-02-01

    We quantify the degradation in performance of a popular and effective face detector when human-perceived image quality is degraded by distortions due to additive white gaussian noise, gaussian blur or JPEG compression. It is observed that, within a certain range of perceived image quality, a modest increase in image quality can drastically improve face detection performance. These results can be used to guide resource or bandwidth allocation in a communication/delivery system that is associated with face detection tasks. A new face detector based on QualHOG features is also proposed that augments face-indicative HOG features with perceptual quality-aware spatial Natural Scene Statistics (NSS) features, yielding improved tolerance against image distortions. The new detector provides statistically significant improvements over a strong baseline on a large database of face images representing a wide range of distortions. To facilitate this study, we created a new Distorted Face Database, containing face and non-face patches from images impaired by a variety of common distortion types and levels. This new dataset is available for download and further experimentation at www.ideal.ece.utexas.edu/˜suriya/DFD/.

  12. 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

  13. 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

  14. 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

  15. Feature weighting algorithms for classification of hyperspectral images using a support vector machine.

    PubMed

    Qi, Bin; Zhao, Chunhui; Yin, Guisheng

    2014-05-01

    The support vector machine (SVM) is a widely used approach for high-dimensional data classification. Traditionally, SVMs use features from the spectral bands of hyperspectral images with each feature contributing equally to the classification. In practical applications, although affected by noise, slight contributions can also be obtained from deteriorated bands. Thus, compared with feature reduction or equal assignment of weights to all the features, feature weighting is a trade-off choice. In this study, we examined two approaches to assigning weights to SVM features to increase the overall classification accuracy: (1) "CSC-SVM" refers to a support vector machine with compactness and a separation coefficient feature weighting algorithm, and (2) "SE-SVM" refers to a support vector machine with a similarity entropy feature weighting algorithm. Analyses were conducted on a public data set with nine selected land-cover classes. In comparison with traditional SVMs and other classical feature weighting algorithms, the proposed weighting algorithms increase the overall classification accuracy, and even better results could be obtained with few training samples. PMID:24921869

  16. Aircraft Detection from VHR Images Based on Circle-Frequency Filter and Multilevel Features

    PubMed Central

    Gao, Feng; 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. PMID:24163637

  17. 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. PMID:24505670

  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. Image features for misalignment correction in medical flat-detector CT

    SciTech Connect

    Wicklein, Julia; Kunze, Holger; Kalender, Willi A.; Kyriakou, Yiannis

    2012-08-15

    Purpose: Misalignment artifacts are a serious problem in medical flat-detector computed tomography. Generally, the geometrical parameters, which are essential for reconstruction, are provided by preceding calibration routines. These procedures are time consuming and the later use of stored parameters is sensitive toward external impacts or patient movement. The method of choice in a clinical environment would be a markerless online-calibration procedure that allows flexible scan trajectories and simultaneously corrects misalignment and motion artifacts during the reconstruction process. Therefore, different image features were evaluated according to their capability of quantifying misalignment. Methods: Projections of the FORBILD head and thorax phantoms were simulated. Additionally, acquisitions of a head phantom and patient data were used for evaluation. For the reconstruction different sources and magnitudes of misalignment were introduced in the geometry description. The resulting volumes were analyzed by entropy (based on the gray-level histogram), total variation, Gabor filter texture features, Haralick co-occurrence features, and Tamura texture features. The feature results were compared to the back-projection mismatch of the disturbed geometry. Results: The evaluations demonstrate the ability of several well-established image features to classify misalignment. The authors elaborated the particular suitability of the gray-level histogram-based entropy on identifying misalignment artifacts, after applying an appropriate window level (bone window). Conclusions: Some of the proposed feature extraction algorithms show a strong correlation with the misalignment level. Especially, entropy-based methods showed very good correspondence, with the best of these being the type that uses the gray-level histogram for calculation. This makes it a suitable image feature for online-calibration.

  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 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

  2. 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.

  3. 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

  4. 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.

  5. Dyke-Davidoff-Masson syndrome: imaging features with illustration of two cases

    PubMed Central

    Rani, Jyotsna Y.

    2015-01-01

    Dyke-Davidoff-Masson syndrome is a rare entity characterized by hemi cerebral atrophy/hypoplasia secondary to brain insult in fetal or early childhood period along with ipsilateral compensatory osseous hypertrophy and contralateral hemiparesis. We present two cases of this uncommon condition and discuss its imaging features, differential diagnosis, treatment options and prognosis. PMID:26029650

  6. Study on Shadow Effects of Various Features on Close Range Thermal Images

    NASA Astrophysics Data System (ADS)

    Liao, C. L.; Huang, H. H.

    2012-07-01

    Thermal infrared data become more popular in remote sensing investigation, for it could be acquired both in day and night. The change of temperature has special characteristic in natural environment, so the thermal infrared images could be used in monitoring volcanic landform, the urban development, and disaster prevention. Heat shadow is formed by reflecting radiating capacity which followed the objects. Because of poor spatial resolution of thermal infrared images in satellite sensor, shadow effects were usually ignored. This research focus on discussing the shadow effects of various features, which include metals and nonmetallic materials. An area-based thermal sensor, FLIR-T360 was selected to acquire thermal images. Various features with different emissivity were chosen as reflective surface to obtain thermal shadow in normal atmospheric temperature. Experiments found that the shadow effects depend on the distance between sensors and features, depression angle, object temperature and emissivity of reflective surface. The causes of shadow effects have been altered in the experiment for analyzing the variance in thermal infrared images. The result shows that there were quite different impacts by shadow effects between metals and nonmetallic materials. The further research would be produced a math model to describe the shadow effects of different features in the future work.

  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. Real-time implementation of optimized maximum noise fraction transform for feature extraction of hyperspectral images

    NASA Astrophysics Data System (ADS)

    Wu, Yuanfeng; Gao, Lianru; Zhang, Bing; Zhao, Haina; Li, Jun

    2014-01-01

    We present a parallel implementation of the optimized maximum noise fraction (G-OMNF) transform algorithm for feature extraction of hyperspectral images on commodity graphics processing units (GPUs). The proposed approach explored the algorithm data-level concurrency and optimized the computing flow. We first defined a three-dimensional grid, in which each thread calculates a sub-block data to easily facilitate the spatial and spectral neighborhood data searches in noise estimation, which is one of the most important steps involved in OMNF. Then, we optimized the processing flow and computed the noise covariance matrix before computing the image covariance matrix to reduce the original hyperspectral image data transmission. These optimization strategies can greatly improve the computing efficiency and can be applied to other feature extraction algorithms. The proposed parallel feature extraction algorithm was implemented on an Nvidia Tesla GPU using the compute unified device architecture and basic linear algebra subroutines library. Through the experiments on several real hyperspectral images, our GPU parallel implementation provides a significant speedup of the algorithm compared with the CPU implementation, especially for highly data parallelizable and arithmetically intensive algorithm parts, such as noise estimation. In order to further evaluate the effectiveness of G-OMNF, we used two different applications: spectral unmixing and classification for evaluation. Considering the sensor scanning rate and the data acquisition time, the proposed parallel implementation met the on-board real-time feature extraction.

  9. 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

  10. Multimodal approach to feature extraction for image and signal learning problems

    NASA Astrophysics Data System (ADS)

    Eads, Damian R.; Williams, Steven J.; Theiler, James; Porter, Reid; Harvey, Neal R.; Perkins, Simon J.; Brumby, Steven P.; David, Nancy A.

    2004-01-01

    We present ZEUS, an algorithm for extracting features from images and time series signals. ZEIS is designed to solve a variety of machine learning problems including time series forecasting, signal classification, image and pixel classification of multispectral and panchromatic imagery. An evolutionary approach is used to extract features from a near-infinite space of possible combinations of nonlinear operators. Each problem type (i.e. signal or image, regression or classification, multiclass or binary) has its own set of primitive operators. We employ fairly generic operators, but note that the choice of which operators to use provides an opportunity to consult with a domain expert. Each feature is produced from a composition of some subset of these primitive operators. The fitness for an evolved set of features is given by the performance of a back-end classifier (or regressor) on training data. We demonstrate our multimodal approach to feature extraction on a variety of problems in remote sensing. The performance of this algorithm will be compared to standard approaches, and the relative benefit of various aspects of the algorithm will be investigated.

  11. 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.

  12. 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.

  13. 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.

  14. An Interactive Technique for Cartographic Feature Extraction from Aerial and Satellite Image Sensors

    PubMed Central

    Kicherer, Stefan; Malpica, Jose A.; Alonso, Maria C.

    2008-01-01

    In this paper, an interactive technique for extracting cartographic features from aerial and spatial images is presented. The method is essentially an interactive method of image region segmentation based on pixel grey level and texture information. The underlying segmentation method is seeded region growing. The criterion for growing regions is based on both texture and grey level, where texture is quantified using co-occurrence matrices. The Kullback distance is utilised with co-occurrence matrices in order to describe the image texture, then the Theory of Evidence is applied to merge the information coming from texture and grey level image from the RGB bands. Several results from aerial and spatial images that support the technique are presented

  15. 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.

  16. 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.

  17. Multi-Modal Ultra-Widefield Imaging Features in Waardenburg Syndrome

    PubMed Central

    Choudhry, Netan; Rao, Rajesh C.

    2015-01-01

    Background Waardenburg syndrome is characterized by a group of features including; telecanthus, a broad nasal root, synophrys of the eyebrows, piedbaldism, heterochromia irides, and deaf-mutism. Hypopigmentation of the choroid is a unique feature of this condition examined with multi-modal Ultra-Widefield Imaging in this report. Material/Methods Report of a single case. Results Bilateral symmetric choroidal hypopigmentation was observed with hypoautofluorescence in the region of hypopigmentation. Fluorescein angiography revealed a normal vasculature, however a thickened choroid was seen on Enhanced-Depth Imaging Spectral-Domain OCT (EDI SD-OCT). Conclusion(s) Choroidal hypopigmentation is a unique feature of Waardenburg syndrome, which can be visualized with ultra-widefield fundus autofluorescence. The choroid may also be thickened in this condition and its thickness measured with EDI SD-OCT. PMID:26114849

  18. Hybrid image representation learning model with invariant features for basal cell carcinoma detection

    NASA Astrophysics Data System (ADS)

    Arevalo, John; Cruz-Roa, Angel; González, Fabio A.

    2013-11-01

    This paper presents a novel method for basal-cell carcinoma detection, which combines state-of-the-art methods for unsupervised feature learning (UFL) and bag of features (BOF) representation. BOF, which is a form of representation learning, has shown a good performance in automatic histopathology image classi cation. In BOF, patches are usually represented using descriptors such as SIFT and DCT. We propose to use UFL to learn the patch representation itself. This is accomplished by applying a topographic UFL method (T-RICA), which automatically learns visual invariance properties of color, scale and rotation from an image collection. These learned features also reveals these visual properties associated to cancerous and healthy tissues and improves carcinoma detection results by 7% with respect to traditional autoencoders, and 6% with respect to standard DCT representations obtaining in average 92% in terms of F-score and 93% of balanced accuracy.

  19. 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

  20. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features.

    PubMed

    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

  1. Large Scale Near-Duplicate Celebrity Web Images Retrieval Using Visual and Textual Features

    PubMed Central

    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. PMID:24163631

  2. 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

  3. Diagnostic imaging of psoriatic arthritis. Part I: etiopathogenesis, classifications and radiographic features.

    PubMed

    Sudoł-Szopińska, Iwona; Matuszewska, Genowefa; Kwiatkowska, Brygida; Pracoń, Grzegorz

    2016-03-01

    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

  4. 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

  5. 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

  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. Multi-features association-based local HOG description for image matching

    NASA Astrophysics Data System (ADS)

    Wu, Bingbing; Zhou, Shilin; Lei, Lin; Ji, Kefeng

    2015-12-01

    Image matching has always been a very important research areas in computer vision. The performance will directly affect the matching results. Among local descriptors, the Scale Invariant Feature Transform(SIFT) is a milestone in image matching, while HOG as an excellent descriptor is widely used in 2D object detection, but it seldom used as a descriptor for matching. In this article, we suppose to pool these algorithms and we use a simple modification of the Rotation- Invariant HOG(RI-HOG) to describe the feature domain detected by SIFT. The RI-HOG is Fourier analyzed in the polar/spherical coordinates. Later in our experiment, we test the performance of our method on a datasets. We are surprised to find that the method outperforms other descriptors in image matching in accuracy.

  8. Imaging in Erdheim-Chester disease: classic features and new insights.

    PubMed

    Moulis, G; Sailler, L; Bonneville, F; Wagner, T

    2014-01-01

    Erdheim-Chester disease is a rare non-Langerhans cell histiocytosis. Osseous involvement is the most frequent feature with bilateral and symmetric osteosclerotic changes in long bone diaphyseal and metaphyseal regions, classically sparing epiphyses. 99mTc scintigraphy shows bilateral and symmetrical metaphysal and diaphyseal increased uptake in almost all the patients, even asymptomatic. Other classical features on CT-scan, very evocative of Erdheim-Chester disease, must be recognised: e.g. 'coated' aorta, 'hairy kidney' patterns. New imaging techniques such as MRI have led to a better description of cardiac and central nervous system involvements. Pachymeningitis and right atrium wall infiltration are new evocative images of this disease. Fluorodeoxyglucose Positron Emission Tomography in the diagnosis or prognosis assessment is still discussed. The objective of this review is to discuss the place of each imaging technique in Erdheim-Chester disease in 2013. PMID:24428974

  9. SAR Image Segmentation Using Voronoi Tessellation and Bayesian Inference Applied to Dark Spot Feature Extraction

    PubMed Central

    Zhao, Quanhua; Li, Yu; Liu, Zhenggang

    2013-01-01

    This paper presents a new segmentation-based algorithm for oil spill feature extraction from Synthetic Aperture Radar (SAR) intensity images. The proposed algorithm combines a Voronoi tessellation, Bayesian inference and Markov Chain Monte Carlo (MCMC) scheme. The shape and distribution features of dark spots can be obtained by segmenting a scene covering an oil spill and/or look-alikes into two homogenous regions: dark spots and their marine surroundings. The proposed algorithm is applied simultaneously to several real SAR intensity images and simulated SAR intensity images which are used for accurate evaluation. The results show that the proposed algorithm can extract the shape and distribution parameters of dark spot areas, which are useful for recognizing oil spills in a further classification stage. PMID:24233074

  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. Association Between Changes in Mammographic Image Features and Risk for Near-Term Breast Cancer Development.

    PubMed

    Tan, Maxine; Zheng, Bin; Leader, Joseph K; Gur, David

    2016-07-01

    The purpose of this study is to develop and test a new computerized model for predicting near-term breast cancer risk based on quantitative assessment of bilateral mammographic image feature variations in a series of negative full-field digital mammography (FFDM) images. The retrospective dataset included series of four sequential FFDM examinations of 335 women. The last examination in each series ("current") and the three most recent "prior" examinations were obtained. All "prior" examinations were interpreted as negative during the original clinical image reading, while in the "current" examinations 159 cancers were detected and pathologically verified and 176 cases remained cancer-free. From each image, we initially computed 158 mammographic density, structural similarity, and texture based image features. The absolute subtraction value between the left and right breasts was selected to represent each feature. We then built three support vector machine (SVM) based risk models, which were trained and tested using a leave-one-case-out based cross-validation method. The actual features used in each SVM model were selected using a nested stepwise regression analysis method. The computed areas under receiver operating characteristic curves monotonically increased from 0.666±0.029 to 0.730±0.027 as the time-lag between the "prior" (3 to 1) and "current" examinations decreases. The maximum adjusted odds ratios were 5.63, 7.43, and 11.1 for the three "prior" (3 to 1) sets of examinations, respectively. This study demonstrated a positive association between the risk scores generated by a bilateral mammographic feature difference based risk model and an increasing trend of the near-term risk for having mammography-detected breast cancer. PMID:26886970

  13. Efficient Markov feature extraction method for image splicing detection using maximization and threshold expansion

    NASA Astrophysics Data System (ADS)

    Han, Jong Goo; Park, Tae Hee; Moon, Yong Ho; Eom, Il Kyu

    2016-03-01

    We propose an efficient Markov feature extraction method for color image splicing detection. The maximum value among the various directional difference values in the discrete cosine transform domain of three color channels is used to choose the Markov features. We show that the discriminability for slicing detection is increased through the maximization process from the point of view of the Kullback-Leibler divergence. In addition, we present a threshold expansion and Markov state decomposition algorithm. Threshold expansion reduces the information loss caused by the coefficient thresholding that is used to restrict the number of Markov features. To compensate the increased number of features due to the threshold expansion, we propose an even-odd Markov state decomposition algorithm. A fixed number of features, regardless of the difference directions, color channels and test datasets, are used in the proposed algorithm. We introduce three kinds of Markov feature vectors. The number of Markov features for splicing detection used in this paper is relatively small compared to the conventional methods, and our method does not require additional feature reduction algorithms. Through experimental simulations, we demonstrate that the proposed method achieves high performance in splicing detection.

  14. 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.

  15. 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.

  16. 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.

  17. Feature Guided Motion Artifact Reduction with Structure-Awareness in 4D CT Images

    PubMed Central

    Han, Dongfeng; Bayouth, John; Song, Qi; Bhatia, Sudershan; Sonka, Milan; Wu, Xiaodong

    2011-01-01

    In this paper, we propose a novel method to reduce the magnitude of 4D CT artifacts by stitching two images with a data-driven regularization constrain, which helps preserve the local anatomy structures. Our method first computes an interface seam for the stitching in the overlapping region of the first image, which passes through the “smoothest” region, to reduce the structure complexity along the stitching interface. Then, we compute the displacements of the seam by matching the corresponding interface seam in the second image. We use sparse 3D features as the structure cues to guide the seam matching, in which a regularization term is incorporated to keep the structure consistency. The energy function is minimized by solving a multiple-label problem in Markov Random Fields with an anatomical structure preserving regularization term. The displacements are propagated to the rest of second image and the two image are stitched along the interface seams based on the computed displacement field. The method was tested on both simulated data and clinical 4D CT images. The experiments on simulated data demonstrated that the proposed method was able to reduce the landmark distance error on average from 2.9 mm to 1.3 mm, outperforming the registration-based method by about 55%. For clinical 4D CT image data, the image quality was evaluated by three medical experts, and all identified much fewer artifacts from the resulting images by our method than from those by the compared method. PMID:22058647

  18. Exploring new quantitative CT image features to improve assessment of lung cancer prognosis

    NASA Astrophysics Data System (ADS)

    Emaminejad, Nastaran; Qian, Wei; Kang, Yan; Guan, Yubao; Lure, Fleming; Zheng, Bin

    2015-03-01

    Due to the promotion of lung cancer screening, more Stage I non-small-cell lung cancers (NSCLC) are currently detected, which usually have favorable prognosis. However, a high percentage of the patients have cancer recurrence after surgery, which reduces overall survival rate. To achieve optimal efficacy of treating and managing Stage I NSCLC patients, it is important to develop more accurate and reliable biomarkers or tools to predict cancer prognosis. The purpose of this study is to investigate a new quantitative image analysis method to predict the risk of lung cancer recurrence of Stage I NSCLC patients after the lung cancer surgery using the conventional chest computed tomography (CT) images and compare the prediction result with a popular genetic biomarker namely, protein expression of the excision repair cross-complementing 1 (ERCC1) genes. In this study, we developed and tested a new computer-aided detection (CAD) scheme to segment lung tumors and initially compute 35 tumor-related morphologic and texture features from CT images. By applying a machine learning based feature selection method, we identified a set of 8 effective and non-redundant image features. Using these features we trained a naïve Bayesian network based classifier to predict the risk of cancer recurrence. When applying to a test dataset with 79 Stage I NSCLC cases, the computed areas under ROC curves were 0.77±0.06 and 0.63±0.07 when using the quantitative image based classifier and ERCC1, respectively. The study results demonstrated the feasibility of improving accuracy of predicting cancer prognosis or recurrence risk using a CAD-based quantitative image analysis method.

  19. 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.

  20. Endocervical Atypical Polypoid Adenomyoma.

    PubMed

    Protopapas, Athanasios; Sotiropoulou, Maria; Athanasiou, Stavros; Loutradis, Dimitrios

    2016-01-01

    Atypical polypoid adenomyomas (APAMs) are rare uterine tumors that occur predominantly in premenopausal women, with less than 250 cases reported so far, worldwide. They may recur after treatment, and they may coexist with, or precede development of an endometrial adenocarcinoma. For this reason cases managed with conservative surgery or medical therapies require long-term follow-up. We report the case of a 41 years old nulliparous patient who during a diagnostic hysteroscopy was found with an endocervical atypical polypoid adenomyoma (APAM). The patient was desirous of a pregnancy, reported menometrorrhagia, and had a coexistent 5 cm, grade 2, submucous myoma, 3 endometrial polyps, and diffuse adenomyosis. She was treated with hysteroscopic resection of the APAM and polyps, plus laparoscopic myomectomy and wedge resection of adenomyosis. She is on an IVF list and after 4 months she is symptoms-free. PMID:26304721

  1. [The modern concept of atypical depression: four definitions].

    PubMed

    Ohmae, Susumu

    2010-01-01

    This report describes and compares four current concepts and definitions of atypical depression. Since its emergence, atypical depression has been considered a depressive state that can be relieved by MAO inhibitors. Davidson classified the symptomatic features of atypical depression into type A, which is predominated by anxiety symptoms, and type V, which is represented by atypical vegetative symptoms, such as hyperphagia, weight gain, oversleeping, and increased sexual drive. Features that are shared by both subtypes include: early onset, female predominance, outpatient predominance, mildness, few suicide attempts, nonbipolarity, nonendogeneity, and few psychomotor changes. Based on these features, bipolar depression can also be defined as atypical depression type V. Herein, we examine and classify four concepts of atypical depression according to the endogenous-nonendogenous (melancholic-nonmelancholic) and unipolar-bipolar dichotomies. The Columbia University group (see Quitkin, Stewart, McGrath, Klein et al.) and the New South Wales University group (see Parker) consider atypical depression to be chronic, mild, nonendogenous (nonmelancholic), unipolar depression. The former group postulates that mood reactivity is necessary, while the latter asserts the structural priority of anxiety symptoms over mood symptoms and the significance of interpersonal rejection sensitivity. For the Columbia group, the significance of mood reactivity reflects the theory that mood nonreactivity is the essential symptom of "endogenomorphic depression", which was proposed by Klein as typical depression. Thus, mood reactivity is not related to overreactivity or hyperactivity, which are often observed in atypical depressives. However, Parker postulates that psychomotor symptoms are the essential features of melancholia, which he recognizes as typical depression; therefore, the New South Wales group does not recognize the significance of mood reactivity. The New South Wales group

  2. Seeing Beyond Sight: The Adaptive, Feature-Specific, Spectral Imaging Classifier

    NASA Astrophysics Data System (ADS)

    Dunlop-Gray, Matthew John

    Spectral imaging, a combination of spectroscopy and imaging, is a powerful tool for providing in situ material classification across a spatial scene. Typically spectral imaging analyses are interested in classification, though conventionally the classification is performed only after reconstruction of the spectral datacube, which can have upwards of 109 signal elements. In this dissertation, I present a computational spectral imaging system, the Adaptive Feature-Specific Spectral Imaging Classifier (AFSSI-C), which yields direct classification across the spatial scene without reconstruction of the source datacube. With a dual disperser architecture and a programmable spatial light modulator which induces spectral filtering, the AFSSI-C measures specific projections of the spectral datacube which in turn feed an adaptive Bayesian classification and feature design framework. I present my work related to the design, construction, and testing of this instrument, which ultimately demonstrated significantly improved classification accuracy compared to legacy spectral imaging systems by first showing agreement with simulation, and then comparing to expected performance of traditional systems. As a result of its open aperture and adaptive filters, the AFSSI-C achieves 250x better accuracy than pushbroom, whiskbroom, and tunable filter systems for a four-class problem at 0 dB TSNR (task signal-to-noise ratio)---a point where measurement noise is equal to the minimum separation between the library spectra. The AFSSI-C also achieves 100x better accuracy than random projections at 0 dB TSNR.

  3. 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.

  4. Synovial sarcoma: Magnetic resonance and computed tomography imaging features and differential diagnostic considerations

    PubMed Central

    LIANG, CHANGHUA; MAO, HUAJIE; TAN, JING; JI, YINGHUA; SUN, FENGXIA; DOU, WENGUANG; WANG, HUIFANG; WANG, HONGPO; GAO, JIANBO

    2015-01-01

    The present study retrospectively examined 24 cases of pathologically confirmed synovial sarcoma and analyzed the clinical presentation and imaging findings in order to explore the imaging features of synovial sarcoma. The majority of the lesions were large (>5 cm; 88%), rounded or lobulated, relatively well-defined tumor masses in the extremities near the joints or deeply located. On computed tomography (CT) scans, the lesions demonstrated intensity signals similar to those of muscle. Six cases exhibited punctate calcification in the periphery of the tumor. On T1-weighted images, the largest lesions of >5 cm, were usually heterogeneous, with a signal intensity similar to or slightly higher than that of muscle. On T2-weighted images, heterogeneous high-intensity or slightly high-intensity signals were observed, with 12 cases exhibiting a high signal consistent with hemorrhage and 12 presenting signals that indicated internal septations. Contrast-enhanced scanning revealed heterogeneous enhancement in the majority of the lesions and no enhancement in areas of cystic necrosis or internal septations. Synovial sarcoma has specific imaging features, and comprehensive analysis of CT and magnetic resonance imaging can improve the accuracy of the pre-operative diagnosis. PMID:25621034

  5. 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.

  6. 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

  7. Imaging features of HER2 overexpression in breast cancer: a systematic review and meta-analysis.

    PubMed

    Elias, Sjoerd G; Adams, Arthur; Wisner, Dorota J; Esserman, Laura J; van't Veer, Laura J; Mali, Willem P Th M; Gilhuijs, Kenneth G A; Hylton, Nola M

    2014-08-01

    Breast cancer imaging phenotype is diverse and may relate to molecular alterations driving cancer behavior. We systematically reviewed and meta-analyzed relations between breast cancer imaging features and human epidermal growth factor receptor type 2 (HER2) overexpression as a marker of breast cancer aggressiveness. MEDLINE and EMBASE were searched for mammography, breast ultrasound, magnetic resonance imaging (MRI), and/or [(18)F]fluorodeoxyglucose positron emission tomography studies through February 2013. Of 68 imaging features that could be pooled (85 articles, 23,255 cancers; random-effects meta-analysis), 11 significantly related to HER2 overexpression. Results based on five or more studies and robustness in subgroup analyses were as follows: the presence of microcalcifications on mammography [pooled odds ratio (pOR), 3.14; 95% confidence interval (CI), 2.46-4.00] or ultrasound (mass-associated pOR, 2.95; 95% CI, 2.34-3.71), branching or fine linear microcalcifications (pOR, 2.11; 95% CI, 1.07-4.14) or extremely dense breasts on mammography (pOR, 1.37; 95% CI, 1.07-1.76), and washout (pOR, 1.57; 95% CI, 1.11-2.21) or fast initial kinetics (pOR, 2.60; 95% CI, 1.43-4.73) on MRI all increased the chance of HER2 overexpression. Maximum [(18)F]fluorodeoxyglucose standardized uptake value (SUVmax) was higher upon HER2 overexpression (pooled mean difference, +0.76; 95% CI, 0.10-1.42). These results show that several imaging features relate to HER2 overexpression, lending credibility to the hypothesis that imaging phenotype reflects cancer behavior. This implies prognostic relevance, which is especially relevant as imaging is readily available during diagnostic work-up. PMID:24807204

  8. Atypical Wernicke's syndrome sans encephalopathy with acute bilateral vision loss due to post-chiasmatic optic tract edema

    PubMed Central

    Desai, Soaham Dilip; Shah, Diva Sidharth

    2014-01-01

    A middle aged male presented with acute bilateral vision loss, 4 weeks after undergoing gastric bypass surgery for gastric carcinoma. He had normal sensorium, fundoscopy, normal pupillary reaction to light, but had mild opthalmoparesis and nystagmus with ataxia. Magnetic resonance imaging of the brain revealed post-chiasmatic optic tract edema along with other classical features of Wernicke's syndrome. Thiamine supplementation leads to complete resolution of clinical as well as imaging findings. In appropriate clinical settings, a high index of suspicion and early treatment are essential for managing Wernicke's syndrome even in patients with atypical clinical and imaging presentation. PMID:24753673

  9. 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

  10. Atypical cases of Dowling-Degos disease.

    PubMed

    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

  11. Regional Image Features Model for Automatic Classification between Normal and Glaucoma in Fundus and Scanning Laser Ophthalmoscopy (SLO) Images.

    PubMed

    Haleem, Muhammad Salman; Han, Liangxiu; Hemert, Jano van; Fleming, Alan; Pasquale, Louis R; Silva, Paolo S; Song, Brian J; Aiello, Lloyd Paul

    2016-06-01

    Glaucoma is one of the leading causes of blindness worldwide. There is no cure for glaucoma but detection at its earliest stage and subsequent treatment can aid patients to prevent blindness. Currently, optic disc and retinal imaging facilitates glaucoma detection but this method requires manual post-imaging modifications that are time-consuming and subjective to image assessment by human observers. Therefore, it is necessary to automate this process. In this work, we have first proposed a novel computer aided approach for automatic glaucoma detection based on Regional Image Features Model (RIFM) which can automatically perform classification between normal and glaucoma images on the basis of regional information. Different from all the existing methods, our approach can extract both geometric (e.g. morphometric properties) and non-geometric based properties (e.g. pixel appearance/intensity values, texture) from images and significantly increase the classification performance. Our proposed approach consists of three new major contributions including automatic localisation of optic disc, automatic segmentation of disc, and classification between normal and glaucoma based on geometric and non-geometric properties of different regions of an image. We have compared our method with existing approaches and tested it on both fundus and Scanning laser ophthalmoscopy (SLO) images. The experimental results show that our proposed approach outperforms the state-of-the-art approaches using either geometric or non-geometric properties. The overall glaucoma classification accuracy for fundus images is 94.4% and accuracy of detection of suspicion of glaucoma in SLO images is 93.9 %. PMID:27086033

  12. Discrimination of retinal images containing bright lesions using sparse coded features and SVM.

    PubMed

    Sidibé, Désiré; Sadek, Ibrahim; Mériaudeau, Fabrice

    2015-07-01

    Diabetic Retinopathy (DR) is a chronic progressive disease of the retinal microvasculature which is among the major causes of vision loss in the world. The diagnosis of DR is based on the detection of retinal lesions such as microaneurysms, exudates and drusen in retinal images acquired by a fundus camera. However, bright lesions such as exudates and drusen share similar appearances while being signs of different diseases. Therefore, discriminating between different types of lesions is of interest for improving screening performances. In this paper, we propose to use sparse coding techniques for retinal images classification. In particular, we are interested in discriminating between retinal images containing either exudates or drusen, and normal images free of lesions. Extensive experiments show that dictionary learning techniques can capture strong structures of retinal images and produce discriminant descriptors for classification. In particular, using a linear SVM with the obtained sparse coded features, the proposed method achieves superior performance as compared with the popular Bag-of-Visual-Word approach for image classification. Experiments with a dataset of 828 retinal images collected from various sources show that the proposed approach provides excellent discrimination results for normal, drusen and exudates images. It achieves a sensitivity and a specificity of 96.50% and 97.70% for the normal class; 99.10% and 100% for the drusen class; and 97.40% and 98.20% for the exudates class with a medium size dictionary of 100 atoms. PMID:25935125

  13. 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

  14. Local Wavelet Pattern: A New Feature Descriptor for Image Retrieval in Medical CT Databases.

    PubMed

    Dubey, Shiv Ram; Singh, Satish Kumar; Singh, Rajat Kumar

    2015-12-01

    A new image feature description based on the local wavelet pattern (LWP) is proposed in this paper to characterize the medical computer tomography (CT) images for content-based CT image retrieval. In the proposed work, the LWP is derived for each pixel of the CT image by utilizing the relationship of center pixel with the local neighboring information. In contrast to the local binary pattern that only considers the relationship between a center pixel and its neighboring pixels, the presented approach first utilizes the relationship among the neighboring pixels using local wavelet decomposition, and finally considers its relationship with the center pixel. A center pixel transformation scheme is introduced to match the range of center value with the range of local wavelet decomposed values. Moreover, the introduced local wavelet decomposition scheme is centrally symmetric and suitable for CT images. The novelty of this paper lies in the following two ways: 1) encoding local neighboring information with local wavelet decomposition and 2) computing LWP using local wavelet decomposed values and transformed center pixel values. We tested the performance of our method over three CT image databases in terms of the precision and recall. We also compared the proposed LWP descriptor with the other state-of-the-art local image descriptors, and the experimental results suggest that the proposed method outperforms other methods for CT image retrieval. PMID:26513789

  15. 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.

  16. Classification of benign and malignant breast masses based on shape and texture features in sonography images.

    PubMed

    Zakeri, Fahimeh Sadat; Behnam, Hamid; Ahmadinejad, Nasrin

    2012-06-01

    The purpose of this research was evaluating novel shape and texture feature' efficiency in classification of benign and malignant breast masses in sonography images. First, mass regions were extracted from the region of interest (ROI) sub-image by implementing a new hybrid segmentation approach based on level set algorithms. Then two left and right side areas of the masses are elicited. After that, six features (Eccentricity_feature, Solidity_feature, DeferenceArea_Hull_Rectangular, DeferenceArea_Mass_Rectangular, Cross-correlation-left and Cross-correlation-right) based on shape, texture and region characteristics of the masses were extracted for further classification. Finally a support vector machine (SVM) classifier was utilized to classify breast masses. The leave-one-case-out protocol was utilized on a database of eighty pathologically-proven breast sonographic images of patients (forty-seven benign cases and thirty-three malignant cases) to evaluate our method. The classification results showed an overall accuracy of 95.00%, sensitivity of 90.91%, specificity of 97.87%, positive predictive value of 96.77%, negative predictive value of 93.88%, and Matthew's correlation coefficient of 89.71%. The experimental results declare that our proposed method is actually a beneficial tool for the diagnosis of the breast cancer and can provide a second opinion for a physician's decision or can be used for the medicine training especially when coupled with other modalities. PMID:21082222

  17. 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.

  18. 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

  19. Splat feature classification with application to retinal hemorrhage detection in fundus images.

    PubMed

    Tang, Li; Niemeijer, Meindert; Reinhardt, Joseph M; Garvin, Mona K; Abràmoff, Michael D

    2013-02-01

    A novel splat feature classification method is presented with application to retinal hemorrhage detection in fundus images. Reliable detection of retinal hemorrhages is important in the development of automated screening systems which can be translated into practice. Under our supervised approach, retinal color images are partitioned into nonoverlapping segments covering the entire image. Each segment, i.e., splat, contains pixels with similar color and spatial location. A set of features is extracted from each splat to describe its characteristics relative to its surroundings, employing responses from a variety of filter bank, interactions with neighboring splats, and shape and texture information. An optimal subset of splat features is selected by a filter approach followed by a wrapper approach. A classifier is trained with splat-based expert annotations and evaluated on the publicly available Messidor dataset. An area under the receiver operating characteristic curve of 0.96 is achieved at the splat level and 0.87 at the image level. While we are focused on retinal hemorrhage detection, our approach has potential to be applied to other object detection tasks. PMID:23193310

  20. 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.

  1. 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.

  2. 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

  3. A statistical-textural-features based approach for classification of solid drugs using surface microscopic images.

    PubMed

    Tahir, Fahima; Fahiem, Muhammad Abuzar

    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

  4. 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.

  5. Automated Breast Image Classification Using Features from Its Discrete Cosine Transform

    PubMed Central

    Kendall, Edward J.; Flynn, Matthew T.

    2014-01-01

    Purpose This work aimed to improve breast screening program accuracy using automated classification. The goal was to determine if whole image features represented in the discrete cosine transform would provide a basis for classification. Priority was placed on avoiding false negative findings. Methods Online datasets were used for this work. No informed consent was required. Programs were developed in Mathematica and, where necessary to improve computational performance ported to C++. The use of a discrete cosine transform to separate normal from cancerous breast tissue was tested. Features (moments of the mean) were calculated in square sections of the transform centered on the origin. K-nearest neighbor and naive Bayesian classifiers were tested. Results Forty-one features were generated and tested singly, and in combination of two or three. Using a k-nearest neighbor classifier, sensitivities as high as 98% with a specificity of 66% were achieved. With a naive Bayesian classifier, sensitivities as high as 100% were achieved with a specificity of 64%. Conclusion Whole image classification based on discrete cosine transform (DCT) features was effectively implemented with a high level of sensitivity and specificity achieved. The high sensitivity attained using the DCT generated feature set implied that these classifiers could be used in series with other methods to increase specificity. Using a classifier with near 100% sensitivity, such as the one developed in this project, before applying a second classifier could only boost the accuracy of that classifier. PMID:24632807

  6. Feature-based pairwise retinal image registration by radial distortion correction

    NASA Astrophysics Data System (ADS)

    Lee, Sangyeol; Abràmoff, Michael D.; Reinhardt, Joseph M.

    2007-03-01

    Fundus camera imaging is widely used to document disorders such as diabetic retinopathy and macular degeneration. Multiple retinal images can be combined together through a procedure known as mosaicing to form an image with a larger field of view. Mosaicing typically requires multiple pairwise registrations of partially overlapped images. We describe a new method for pairwise retinal image registration. The proposed method is unique in that the radial distortion due to image acquisition is corrected prior to the geometric transformation. Vessel lines are detected using the Hessian operator and are used as input features to the registration. Since the overlapping region is typically small in a retinal image pair, only a few correspondences are available, thus limiting the applicable model to an afine transform at best. To recover the distortion due to curved-surface of retina and lens optics, a combined approach of an afine model with a radial distortion correction is proposed. The parameters of the image acquisition and radial distortion models are estimated during an optimization step that uses Powell's method driven by the vessel line distance. Experimental results using 20 pairs of green channel images acquired from three subjects with a fundus camera confirmed that the afine model with distortion correction could register retinal image pairs to within 1.88+/-0.35 pixels accuracy (mean +/- standard deviation) assessed by vessel line error, which is 17% better than the afine-only approach. Because the proposed method needs only two correspondences, it can be applied to obtain good registration accuracy even in the case of small overlap between retinal image pairs.

  7. 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.

  8. 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

  9. 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