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Sample records for atypical imaging features

  1. Upgrade Rate and Imaging Features of Atypical Apocrine Lesions.

    PubMed

    Chang Sen, Lauren Q; Berg, Wendie A; Carter, Gloria J

    2017-03-23

    The purpose of our work was to identify imaging features of atypical apocrine lesions and determine the rate of upgrade to ductal carcinoma in situ (DCIS) or invasive carcinoma at excision after such a diagnosis on percutaneous breast biopsy. From January 1, 2006, through October 8, 2013, a total of 33,157 breast core biopsies were performed at University of Pittsburgh Medical Center. Of those, 58 (0.2%) showed atypical apocrine lesions. For 24, atypical apocrine adenosis (AAA) or atypical apocrine metaplasia (AAM) was the only risk lesion, with no known ipsilateral malignancy, and the results of excision were available. The median patient age was 58 years (range 43-88). Among 24 atypical apocrine lesions (20 AAA and 4 AAM), four (16.7%; 95% confidence interval: 4.7, 37.4) were upgraded at excision: one invasive ductal carcinoma (grade 2, 0.2 cm, estrogen receptor positive, progesterone receptor positive, HER2/Neu negative) and three DCIS (two grade 3, one grade 2). All four upgraded lesions were AAA (20%; 4/20). Twelve AAA were seen as an irregular (n = 9) or circumscribed (n = 3) mass on ultrasound; three masses had calcifications. Six of 20 (30%) AAA were seen on biopsy of calcifications only and calcifications were within two AAA lesions at histopathology. One AAA (1/20, 5%) was asymmetry only, and one (1/20, 5%) a persistently enhancing MR focus. All four malignancies were masses on ultrasound (three irregular, one circumscribed), and three malignancies had calcifications (two coarse heterogeneous, one amorphous). While concordant with an irregular or circumscribed mass on imaging, with or without amorphous or coarse heterogeneous calcifications, AAA merits excision with a 20% upgrade rate to malignancy. Further study of AAM is warranted.

  2. 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

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

    PubMed

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

    2015-01-01

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

  5. 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.

  6. 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.

  7. 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

  8. Atypical choroid plexus papilloma: clinicopathological and neuroradiological features.

    PubMed

    Shi, Yu-Zhen; Chen, Mao-Zhen; Huang, Wei; Guo, Li-Li; Chen, Xiao; Kong, Dan; Zhuang, Ying-Ying; Xu, Yi-Ming; Zhang, Rui-Rui; Bo, Gen-Ji; Wang, Zhong-Qiu

    2017-01-01

    Background Atypical choroid plexus papilloma (APP) is a rare, newly introduced entity with intermediate characteristics. To date, few reports have revealed the magnetic resonance (MR) findings. Purpose To analyze the clinicopathological and MR features of APP. Material and Methods The clinicopathological data and preoperative MR images of six patients with pathologically proven APP were retrospectively reviewed. The MR features including tumor location, contour, signal intensity, degree of enhancement, intratumoral cysts, and necrosis; and flow voids, borders, peritumoral edema, and associated hydrocephalus were analyzed. Results The APP were located in the ventricle (n = 4) and cerebellopontine angle (CPA, n = 2). Tumor dissemination along the spinal subarachnoid space was found in one patient. The tumors appeared as milt-lobulated (n = 5) or round mass (n = 1), with slightly heterogeneous signals (n = 5) or mixed signals (n = 1) on T1-weighted and T2-weighted images. Heterogeneous and strong enhancement were found in five cases on contrast-enhanced images. Three of four intraventricular tumors had a partly blurred border with ventricle wall. Four tumors had mild to moderate extent of surrounding edema signals. A slight hydrocephalus was seen in four patients. Incomplete capsule was seen in four tumors at surgery. Histopathologically, mild nuclear atypia was seen in all tumors with a mitotic rate of 2-5 per 10 high-power fields. Conclusion APP should be included in the differential diagnosis when an intraventricular or CPA tumor appearing as a multi-lobulated solid mass with slight heterogeneity, heterogeneous strong enhancement, partly blurred borders, mild to moderate peritumoral edema, or slight hydrocephalus are present.

  9. Wernicke encephalopathy with atypical magnetic resonance imaging.

    PubMed

    Liou, Kuang-Chung; Kuo, Shu-Fan; Chen, Lu-An

    2012-11-01

    Wernicke encephalopathy (WE) is a medical emergency caused by thiamine (vitamin B1) deficiency. Typical clinical manifestations are mental change, ataxia, and ocular abnormalities. Wernicke encephalopathy is an important differential diagnosis in all patients with acute mental change. However, the disorder is greatly underdiagnosed. Clinical suspicion, detailed history taking, and neurologic evaluations are important for early diagnosis. Magnetic resonance imaging (MRI) is currently considered the diagnostic method of choice. Typical MRI findings of WE are symmetrical involvement of medial thalamus, mammillary body, and periaqueductal gray matter. Prompt thiamine supplement is important in avoiding unfavorable outcomes. Here, we report a case of alcoholic WE with typical clinical presentation but with atypical MRI. Axial fluid-attenuated inversion recovery images showing symmetrical hyperintensity lesions in dentate nuclei of cerebellum, olivary bodies, and dorsal pons. Although atypical MRI findings are more common in nonalcoholic WE, it can also occur in alcoholic WE. This article is aimed to highlight the potential pitfalls in diagnosing acute mental change, the importance of clinical suspicion, and early treatment in WE.

  10. 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.

  11. Atypical Depression

    MedlinePlus

    Diseases and Conditions Atypical depression By Mayo Clinic Staff Any type of depression can make you feel sad and keep you from enjoying life. However, atypical depression — also called depression with atypical features — means that ...

  12. Hepatic haemangioma: common and uncommon imaging features.

    PubMed

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

    2013-09-01

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

  13. [Cerebral hydatid disease: imaging features].

    PubMed

    Tlili-Graiess, K; El-Ouni, F; Gharbi-Jemni, H; Arifa, N; Moulahi, H; Mrad-Dali, K; Guesmi, H; Abroug, S; Yacoub, M; Krifa, H

    2006-12-01

    Cerebral hytatid cysts (HC) are extremely rare, forming 2% of all intra cranial space occupying lesions even in counties where the disease is endemic. HC diagnosis is usually based on a pathognomonic computed tomography (CT) pattern. In order to assess the value of MR we reviewed the CT (n=25) and magnetic resonance (MR, n=4 including diffusion and proton magnetic resonance spectroscopy in 1) imaging of 25 patients with pathologically confirmed cerebral hydatid disease. 19 HC were seen in children under 16 years. All were supra tentorial with 22 in the middle cerebral artery territory. HC was solitary in 18 cases, unilocular in 23 and multi-vesicular in 2 with heavily calcified pericyst in 1. 2 cysts were intra ventricular and 1 intra aqueducal. The most typical features were well defined, smooth thin walled spherical or oval cystic lesions of CSF density and/or signal with considerable mass effect (20/25). Surrounding oedema with complete or incomplete rim enhancement was seen in 3 cases which were labelled as complicated and/or infected cysts. Although CT is diagnostic of hydatid disease in almost all cases (22/25), MRI including diffusion and spectroscopy precisely demonstrate location, number, cyst capsule, type of signal and enhancement and allows diagnosis of atypical or complicated HC and appears more helpful in surgical planning.

  14. Unusual imaging presentation of infantile atypical Kawasaki disease.

    PubMed

    Kumar, Nishith; Mittal, Mahesh Kumar; Sinha, Mukul; Gupta, Arpita; Thukral, Brij Bhushan

    2016-01-01

    Kawasaki disease is a systemic medium vessel vasculitis of unknown etiology affecting children under 5 years of age. There are no specific diagnostic tests, and thus, the diagnosis of the disease is primarily made on the basis of clinical criteria. Unusual presentations of Kawasaki disease have been variably reported from different parts of the world. However, presentation of the disease in the form of peripheral thromboembolism and florid non-coronary aneurysms has rarely been described This report describes the imaging findings in infantile atypical Kawasaki disease with aneurysms of multiple medium-sized arteries, including coronary arteries, emphasizing the detection of clinically silent aneurysms in the disease.

  15. 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.

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

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

    2015-12-01

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

  18. Identification of an atypical etiological head and neck squamous carcinoma subtype featuring the CpG island methylator phenotype.

    PubMed

    Brennan, K; Koenig, J L; Gentles, A J; Sunwoo, J B; Gevaert, O

    2017-03-01

    Head and neck squamous cell carcinoma (HNSCC) is broadly classified into HNSCC associated with human papilloma virus (HPV) infection, and HPV negative HNSCC, which is typically smoking-related. A subset of HPV negative HNSCCs occur in patients without smoking history, however, and these etiologically 'atypical' HNSCCs disproportionately occur in the oral cavity, and in female patients, suggesting a distinct etiology. To investigate the determinants of clinical and molecular heterogeneity, we performed unsupervised clustering to classify 528 HNSCC patients from The Cancer Genome Atlas (TCGA) into putative intrinsic subtypes based on their profiles of epigenetically (DNA methylation) deregulated genes. HNSCCs clustered into five subtypes, including one HPV positive subtype, two smoking-related subtypes, and two atypical subtypes. One atypical subtype was particularly genomically stable, but featured widespread gene silencing associated with the 'CpG island methylator phenotype' (CIMP). Further distinguishing features of this 'CIMP-Atypical' subtype include an antiviral gene expression profile associated with pro-inflammatory M1 macrophages and CD8+ T cell infiltration, CASP8 mutations, and a well-differentiated state corresponding to normal SOX2 copy number and SOX2OT hypermethylation. We developed a gene expression classifier for the CIMP-Atypical subtype that could classify atypical disease features in two independent patient cohorts, demonstrating the reproducibility of this subtype. Taken together, these findings provide unprecedented evidence that atypical HNSCC is molecularly distinct, and postulates the CIMP-Atypical subtype as a distinct clinical entity that may be caused by chronic inflammation.

  19. A patient with amyotrophic lateral sclerosis and atypical clinical and electrodiagnostic features: a case report

    PubMed Central

    2011-01-01

    Introduction Amyotrophic lateral sclerosis is a rapidly progressive, fatal neurodegenerative disorder for which there is no effective treatment. The diagnosis is dependent on the clinical presentation and consistent electrodiagnostic studies. Typically, there is a combination of upper and lower motor neuron signs as well as electrodiagnostic studies indicative of diffuse motor axonal injury. The presentation of amyotrophic lateral sclerosis, however, may be variable. At the same time, the diagnosis is essential for patient prognosis and management. It is therefore important to appreciate the range of possible presentations of amyotrophic lateral sclerosis. Case presentation We present the case of a 57-year-old Caucasian man with pathological findings on postmortem examination consistent with amyotrophic lateral sclerosis but atypical clinical and electrodiagnostic features. He died after a rapid course of progressive weakness. The patient did not respond to immunosuppressive therapy. Conclusion Amyotrophic lateral sclerosis should be considered in patients with a rapidly progressive, unexplained neuropathic process. This should be true even if there are atypical clinical and electrodiagnostic findings. Absence of response to therapy and the development of upper motor neuron signs should reinforce the possibility that amyotrophic lateral sclerosis may be present. Since amyotrophic lateral sclerosis is a fatal illness, however, the possibility of this disease in patients with atypical clinical features should not diminish the need for a thorough diagnostic evaluation and treatment trials. PMID:22047468

  20. Molecular imaging to track Parkinson's disease and atypical parkinsonisms: New imaging frontiers.

    PubMed

    Strafella, Antonio P; Bohnen, Nicolaas I; Perlmutter, Joel S; Eidelberg, David; Pavese, Nicola; Van Eimeren, Thilo; Piccini, Paola; Politis, Marios; Thobois, Stephane; Ceravolo, Roberto; Higuchi, Makoto; Kaasinen, Valtteri; Masellis, Mario; Peralta, M Cecilia; Obeso, Ignacio; Pineda-Pardo, Jose Ángel; Cilia, Roberto; Ballanger, Benedicte; Niethammer, Martin; Stoessl, Jon A

    2017-02-01

    Molecular imaging has proven to be a powerful tool for investigation of parkinsonian disorders. One current challenge is to identify biomarkers of early changes that may predict the clinical trajectory of parkinsonian disorders. Exciting new tracer developments hold the potential for in vivo markers of underlying pathology. Herein, we provide an overview of molecular imaging advances and how these approaches help us to understand PD and atypical parkinsonisms. © 2016 International Parkinson and Movement Disorder Society.

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

    PubMed

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

    2014-10-01

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

  2. [Imaging features of CNS tuberculosis].

    PubMed

    Semlali, S; El Kharras, A; Mahi, M; Hsaini, Y; Benameur, M; Aziz, N; Chaouir, S; Akjouj, S

    2008-02-01

    CNS tuberculosis remains relatively frequent in endemic regions. Both CT and MRI are valuable for diagnosis. Even though non-specific, MRI including diffusion-weighted imaging and proton spectroscopy is more sensitive than CT for detection of some lesions. The purpose of this paper is to illustrate the imaging features of CNS tuberculosis.

  3. 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.

  4. Atypical sensory sensitivity as a shared feature between synaesthesia and autism.

    PubMed

    Ward, Jamie; Hoadley, Claire; Hughes, James E A; Smith, Paula; Allison, Carrie; Baron-Cohen, Simon; Simner, Julia

    2017-03-07

    Several studies have suggested that there is a link between synaesthesia and autism but the nature of that link remains poorly characterised. The present study considers whether atypical sensory sensitivity may be a common link between the conditions. Sensory hypersensitivity (aversion to certain sounds, touch, etc., or increased ability to make sensory discriminations) and/or hyposensitivity (desire to stimulate the senses , or a reduced response to sensory stimuli are a recently introduced diagnostic feature of autism spectrum conditions (ASC). Synaesthesia is defined by unusual sensory experiences and has also been linked to a typical cortical hyper-excitability. The Glasgow Sensory Questionnaire (GSQ) was administered to synaesthetes and people with ASC. Both groups reported increased sensory sensitivity relative to controls with a large effect size. Both groups also reported a similar pattern of both increased hyper- and hypo-sensitivities across multiple senses. The AQ (Autism-Spectrum Quotient) scores were elevated in the synaesthetes, and one subscale of this measure (attention to detail) placed synaesthetes within the autistic range. A standard laboratory test of visual stress (the Pattern Glare Test), administered online, corroborated the findings of increased sensitivity to aversive visual stimuli in synaesthetes. We conclude that atypical sensory sensitivity is an important shared feature between autism and synaesthesia.

  5. Atypical sensory sensitivity as a shared feature between synaesthesia and autism

    PubMed Central

    Ward, Jamie; Hoadley, Claire; Hughes, James E. A.; Smith, Paula; Allison, Carrie; Baron-Cohen, Simon; Simner, Julia

    2017-01-01

    Several studies have suggested that there is a link between synaesthesia and autism but the nature of that link remains poorly characterised. The present study considers whether atypical sensory sensitivity may be a common link between the conditions. Sensory hypersensitivity (aversion to certain sounds, touch, etc., or increased ability to make sensory discriminations) and/or hyposensitivity (desire to stimulate the senses , or a reduced response to sensory stimuli are a recently introduced diagnostic feature of autism spectrum conditions (ASC). Synaesthesia is defined by unusual sensory experiences and has also been linked to a typical cortical hyper-excitability. The Glasgow Sensory Questionnaire (GSQ) was administered to synaesthetes and people with ASC. Both groups reported increased sensory sensitivity relative to controls with a large effect size. Both groups also reported a similar pattern of both increased hyper- and hypo-sensitivities across multiple senses. The AQ (Autism-Spectrum Quotient) scores were elevated in the synaesthetes, and one subscale of this measure (attention to detail) placed synaesthetes within the autistic range. A standard laboratory test of visual stress (the Pattern Glare Test), administered online, corroborated the findings of increased sensitivity to aversive visual stimuli in synaesthetes. We conclude that atypical sensory sensitivity is an important shared feature between autism and synaesthesia. PMID:28266503

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

    PubMed

    Miyata, Shigeki; Maeda, Takuma

    2016-03-01

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

  7. [Toxoplasmosis in French Guiana. Atypical (neo-)tropical features of a cosmopolitan parasitosis].

    PubMed

    Carme, B; Demar-Pierre, M

    2006-10-01

    Toxoplasmosis, a typical cosmopolitan parasitosis, is a major health problem in French Guiana. Three factors account for this situation, i.e., (1) severity of toxoplasmosis in patients with HIV infection that is particularly prevalent in the area, (2) high risk of congenital transmission as shown by the steadily increasing prevalence of seropositivity in function of age in most of the Guianese population and (3) recent identification of severe primary toxoplasmosis infection in immunocompetent patients. In AIDS patients, the epidemiologic aspects of toxoplasmosis are correlated to the special features of the HIV-positive population in French Guiana and its clinical expression, mainly in the form of cerebral toxoplasmosis, does not suggest involvement of a particularly virulent strain of Toxoplasma. Similarly congenital toxoplasmosis does not present special tropical features other than problems associated with prevention, diagnosis and follow-up in poor and/or remote settings. These features are fully compatible with the classic domestic cat cycle of Toxoplasma gondii. However severe forms of primary infection, particularly in immunocompetent adults, appear to be associated with atypical features. These forms appear to be correlated with a forest-based cycle involving wild cats, which are still numerous in French Guiana, and their prey. Ingestion of undercooked wildcat prey, which is also a delicacy for man, can also be a source of contamination as can be consumption of untreated river water infected with oocysts excreted by felines. Observation of higher toxoplasmosis seroprevalence in wild noncarnivorous mammals that live by foraging on the ground in uninhabited forest zones suggests that infection can also be due to ingestion of oocysts eliminated into the soil. Since there are no domestic cats in the area, it must be assumed that these oocysts are shed by wild felines. More convincing proof can be seen in the fact that T. gondi strains presenting polymorphism

  8. 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.

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

    PubMed

    Zhang, Huijun; Lu, Zenghui

    2016-01-01

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

  10. 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

  11. Featured Image: A Comet's Coma

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2016-11-01

    This series of images (click for the full view!) features the nucleus of comet 67P/Churymov-Gerasimenko. The images were taken with the Wide Angle Camera of RosettasOSIRIS instrument asRosetta orbited comet 67P. Each column represents a different narrow-band filter that allows us to examine the emission of a specific fragment species, and the images progress in time from January 2015 (top) to June 2015 (bottom). In a recent study, Dennis Bodewits (University of Maryland) and collaborators used these images to analyze the comets inner coma, the cloud of gas and dust produced around the nucleus as ices sublime. OSIRISs images allowed the team to explore how the 67Ps inner coma changed over time as the comet approached the Sun marking the first time weve been able to study such an environment at this level of detail. To read more about what Bodewits and collaborators learned, you can check out their paper below!CitationD. Bodewits et al 2016 AJ 152 130. doi:10.3847/0004-6256/152/5/130

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

    PubMed

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

    2008-03-01

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

  13. "Atypical" atypical parkinsonism: new genetic conditions presenting with features of progressive supranuclear palsy, corticobasal degeneration, or multiple system atrophy-a diagnostic guide.

    PubMed

    Stamelou, Maria; Quinn, Niall P; Bhatia, Kailash P

    2013-08-01

    Recently, a number of genetic parkinsonian conditions have been recognized that share some features with the clinical syndromes of progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), and multiple system atrophy (MSA), the classic phenotypic templates of atypical parkinsonism. For example, patients with progranulin, dynactin, or ATP13A gene mutations may have vertical supranuclear gaze palsy. This has made differential diagnosis difficult for practitioners. In this review, our goal is to make clinicians aware of these genetic disorders and provide clinical clues and syndromic associations, as well as investigative features, that may help in diagnosing these disorders. The correct identification of these patients has important clinical, therapeutic, and research implications. © 2013 Movement Disorder Society.

  14. Infrared image mosaic using point feature operators

    NASA Astrophysics Data System (ADS)

    Huang, Zhen; Sun, Shaoyuan; Shen, Zhenyi; Hou, Junjie; Zhao, Haitao

    2016-10-01

    In this paper, we study infrared image mosaic around a single point of rotation, aiming at expanding the narrow view range of infrared images. We propose an infrared image mosaic method using point feature operators including image registration and image synthesis. Traditional mosaic algorithms usually use global image registration methods to extract the feature points in the global image, which cost too much time as well as considerable matching errors. To address this issue, we first roughly calculate the image shift amount using phase correlation and determine the overlap region between images, and then extract image features in overlap region, which shortens the registration time and increases the quality of feature points. We improve the traditional algorithm through increasing constraints of point matching based on prior knowledge of image shift amount based on which the weighted map is computed using fade in-out method. The experimental results verify that the proposed method has better real time performance and robustness.

  15. Identifying Image Manipulation Software from Image Features

    DTIC Science & Technology

    2015-03-26

    6 2.1 Mathematical Image Definition , Image Formats, and Interpolation Algorithms...6 2.1.1 Mathematical Image Definition ...Farid [9]. The following section defines the mathematical definition of an image, discusses two image formats, and three interpolation algo- rithms. 2.1

  16. Atypical presentations of orbital cysticercosis.

    PubMed

    Pushker, Neelam; Chaturvedi, Amrita; Balasubramanya, Ramamurthy; Bajaj, Mandeep S; Kumar, Neena; Sony, Parul

    2005-01-01

    We describe three patients with orbital cysticercosis who presented with atypical clinical or radiologic features previously unreported. All three patients had a cyst with a scolex on imaging studies. After 6 weeks of treatment, all three had almost complete resolution of their features.

  17. Radar Imaging and Feature Extraction

    DTIC Science & Technology

    2007-11-02

    aperture radar (ISAR) autofocus and imaging, synthetic aperture radar (SAR) autofocus and motion compensation, superresolution SAR image formation... superresolution image formation, and two parametric methods, MCRELAX (Motion Compensation RELAX) and MCCLEAN (Motion Compensation CLEAN), for simultaneous target...Direction Estimation) together with WRELAX) algorithm is proposed for the superresolution time delay estimation.

  18. Image Algebra Application to Image Measurement and Feature Extraction

    NASA Astrophysics Data System (ADS)

    Ritter, Gerhard X.; Wilson, Joseph N.; Davidson, Jennifer L.

    1989-03-01

    It has been well established that the AFATL (Air Force Armament Technical Laboratory) Image Algebra is capable of expressing all image-to-image transformations [1,2] and that it is ideally suited for parallel image transformations {3,4]. In this paper we show how the algebra can also be applied to compactly express image-to-feature transforms including such sequential image-to-feature transforms as chain coding.

  19. [Medical image retrieval by high level semantic features and low level content features of image].

    PubMed

    Xie, Tianwen; Tang, Weijun; Zhao, Qiufeng; Zhao, Jiaao

    2009-12-01

    Content-based image retrieval aims at searching the similar images using low level features,and medical image retrieval needs it for the retrieval of similar images. Medical images contain not only a lot of content data, but also a lot of semantic information. This paper presents an approach by combining digital imaging and communications in medicine (DICOM) features and low level features to perform retrieval on medical image databases. At the first step, the semantic information is extracted from DICOM header for the pre-filtering of the images, and then dual-tree complex wavelet transfrom(DT-CWT) features of pre-filtered images and example images are extracted to retrieve similar images. Experimental results show that by combining the high level semantics (DICOM features) and low level content features (texture) the retrieval time is reduced and the performance of medical image retrieval is increased.

  20. Local feature point extraction for quantum images

    NASA Astrophysics Data System (ADS)

    Zhang, Yi; Lu, Kai; Xu, Kai; Gao, Yinghui; Wilson, Richard

    2015-05-01

    Quantum image processing has been a hot issue in the last decade. However, the lack of the quantum feature extraction method leads to the limitation of quantum image understanding. In this paper, a quantum feature extraction framework is proposed based on the novel enhanced quantum representation of digital images. Based on the design of quantum image addition and subtraction operations and some quantum image transformations, the feature points could be extracted by comparing and thresholding the gradients of the pixels. Different methods of computing the pixel gradient and different thresholds can be realized under this quantum framework. The feature points extracted from quantum image can be used to construct quantum graph. Our work bridges the gap between quantum image processing and graph analysis based on quantum mechanics.

  1. Examination of Huntington's disease with atypical clinical features in a Bangladeshi family tree.

    PubMed

    Al-Mamun, Md Mahfuz; Sarker, Suprovath Kumar; Qadri, Syeda Kashfi; Shirin, Tahmina; Mohammad, Quazi Deen; LaRocque, Regina; Karlsson, Elinor K; Saha, Narayan; Asaduzzaman, Muhammad; Qadri, Firdausi; Mannoor, Md Kaiissar

    2016-12-01

    Atypical manifestation of Huntington's disease (HD) could inform ongoing research into HD genetic modifiers not present in the primarily European populations studied to date. This work demonstrates that expanding HD genetic testing into under-resourced healthcare settings can benefit both local communities and ongoing research into HD etiology and new therapies.

  2. Image fusion using sparse overcomplete feature dictionaries

    DOEpatents

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

    2015-10-06

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

  3. Image segmentation using association rule features.

    PubMed

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

    2002-01-01

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

  4. Imaging features of iliopsoas bursitis.

    PubMed

    Wunderbaldinger, P; Bremer, C; Schellenberger, E; Cejna, M; Turetschek, K; Kainberger, F

    2002-02-01

    The aim of this study was firstly to describe the spectrum of imaging findings seen in iliopsoas bursitis, and secondly to compare cross-sectional imaging techniques in the demonstration of the extent, size and appearance of the iliopsoas bursitis as referenced by surgery. Imaging studies of 18 patients (13 women, 5 men; mean age 53 years) with surgically proven iliopsoas bursitis were reviewed. All patients received conventional radiographs of the pelvis and hip, US and MR imaging of the hip. The CT was performed in 5 of the 18 patients. Ultrasound, CT and MR all demonstrated enlarged iliopsoas bursae. The bursal wall was thin and well defined in 83% and thickened in 17% of all cases. The two cases with septations on US were not seen by CT and MRI. A communication between the bursa and the hip joint was seen, and surgically verified, in all 18 patients by MR imaging, whereas US and CT failed to demonstrate it in 44 and 40% of the cases, respectively. Hip joint effusion was seen and verified by surgery in 16 patients by MRI, whereas CT (4 of 5) and US ( n=12) underestimated the number. The overall size of the bursa corresponded best between MRI and surgery, whereas CT and US tended to underestimate the size. Contrast enhancement of the bursal wall was seen in all cases. The imaging characteristics of iliopsoas bursitis are a well-defined, thin-walled cystic mass with a communication to the hip joint and peripheral contrast enhancement. The most accurate way to assess iliopsoas bursitis is with MR imaging; thus, it should be used for accurate therapy planning and follow-up studies. In order to initially prove an iliopsoas bursitis, US is the most cost-effective, easy-to-perform and fast alternative.

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

    PubMed

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

    2013-01-01

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

  6. 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

  7. Detection of linear features in aerial images

    NASA Astrophysics Data System (ADS)

    Gao, Rui

    Over the past decades, considerable progress had been made to develop automatic image interpretation tools in remote sensing. However, there is still a gap between the results and the requirements for accuracy and robustness. Noisy aerial image interpretation, especially for low resolution images, is still difficult. In this thesis, we propose a fully automatic system for linear feature detection in aerial images. We present how the system works on the application of extraction and reconstruction of road and pipeline networks. The work in this thesis is divided by three parts: line detection, feature interpretation, and feature tracking. An improved Hough transform based on orientation information is introduced for the line detection. We explore the Markov random field model and Bayesian filtering for feature interpretation and tracking. Experimental results show that our proposed system is robust and effective to deal with low resolution aerial images.

  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: Simulating Planetary Gaps

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2017-03-01

    The authors model of howthe above disk would look as we observe it in a scattered-light image. The morphology of the gap can be used to estimate the mass of the planet that caused it. [Dong Fung 2017]The above image from a computer simulation reveals the dust structure of a protoplanetary disk (with the star obscured in the center) as a newly formed planet orbits within it. A recent study by Ruobing Dong (Steward Observatory, University of Arizona) and Jeffrey Fung (University of California, Berkeley) examines how we can determine mass of such a planet based on our observations of the gap that the planet opens in the disk as it orbits. The authors models help us to better understand how our observations of gaps might change if the disk is inclined relative to our line of sight, and how we can still constrain the mass of the gap-opening planet and the viscosity of the disk from the scattered-light images we have recently begun to obtain of distant protoplanetary disks. For more information, check out the paper below!CitationRuobing Dong () and Jeffrey Fung () 2017 ApJ 835 146. doi:10.3847/1538-4357/835/2/146

  10. 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.

  11. Diffusion tensor imaging of Parkinson's disease, atypical parkinsonism, and essential tremor.

    PubMed

    Prodoehl, Janey; Li, Hong; Planetta, Peggy J; Goetz, Christopher G; Shannon, Kathleen M; Tangonan, Ruth; Comella, Cynthia L; Simuni, Tanya; Zhou, Xiaohong Joe; Leurgans, Sue; Corcos, Daniel M; Vaillancourt, David E

    2013-11-01

    Diffusion tensor imaging could be useful in characterizing movement disorders because it noninvasively examines multiple brain regions simultaneously. We report a multitarget imaging approach focused on the basal ganglia and cerebellum in Parkinson's disease, parkinsonian variant of multiple system atrophy, progressive supranuclear palsy, and essential tremor and in healthy controls. Seventy-two subjects were studied with a diffusion tensor imaging protocol at 3 Tesla. Receiver operating characteristic analysis was performed to directly compare groups. Sensitivity and specificity values were quantified for control versus movement disorder (92% sensitivity, 88% specificity), control versus parkinsonism (93% sensitivity, 91% specificity), Parkinson's disease versus atypical parkinsonism (90% sensitivity, 100% specificity), Parkinson's disease versus multiple system atrophy (94% sensitivity, 100% specificity), Parkinson's disease versus progressive supranuclear palsy (87% sensitivity, 100% specificity), multiple system atrophy versus progressive supranuclear palsy (90% sensitivity, 100% specificity), and Parkinson's disease versus essential tremor (92% sensitivity, 87% specificity). The brain targets varied for each comparison, but the substantia nigra, putamen, caudate, and middle cerebellar peduncle were the most frequently selected brain regions across classifications. These results indicate that using diffusion tensor imaging of the basal ganglia and cerebellum accurately classifies subjects diagnosed with Parkinson's disease, atypical parkinsonism, and essential tremor and clearly distinguishes them from control subjects.

  12. 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.

  13. 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

  14. Atypical melanosis of the foot showing a dermoscopic feature of the parallel ridge pattern.

    PubMed

    Kilinc Karaarslan, Isil; Akalin, Taner; Unal, Idil; Ozdemir, Fezal

    2007-01-01

    A 62-year-old male Turkish patient had a pigmented lesion on the sole with a 10-year history. It was an asymmetrical macular lesion with an irregular border and irregular brown pigmentation and had a diameter of 1.2 cm x 1.7 cm. Dermoscopy revealed a parallel ridge pattern and an abrupt cut-off of pigmentation on the upper edge. Histologically lentiginous hyperplasia decorated by innocent melanocytes and scattered melanocytic proliferation with slight to moderate cytological atypia were seen. Atypical melanocytes were very scattered and it was insufficient to call it a melanoma in situ. A second finding was a microvascular proliferation located in the papillary dermis. There was no sign of regression such as fibrous tissue or host reaction. Atypical melanosis of the foot has rarely been reported in the published work, which are from Japan and Korea. This case is presented to emphasize the significance of this rare entity which has recently been reported to be a very early phase of acral melanoma.

  15. Feature utility in polarimetric radar image classification

    NASA Technical Reports Server (NTRS)

    Cumming, Ian G.; Van Zyl, Jakob J.

    1989-01-01

    The information content in polarimetric SAR images is examined, and the polarimetric image variables containing the information that is important to the classification of terrain features in the images are determined. It is concluded that accurate classification can be done when just over half of the image variables are retained. A reduction in image data dimensionality gives storage savings, and can lead to the improvement of classifier performance. In addition, it is shown that a simplified radar system with only phase-calibrated CO-POL or SINGLE TX channels can give classification performance which approaches that of a fully polarimetric radar.

  16. Detecting Nematode Features from Digital Images

    PubMed Central

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

    1992-01-01

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

  17. Atypical Cities

    ERIC Educational Resources Information Center

    DiJulio, Betsy

    2011-01-01

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

  18. 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.

  19. 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.

  20. On image matrix based feature extraction algorithms.

    PubMed

    Wang, Liwei; Wang, Xiao; Feng, Jufu

    2006-02-01

    Principal component analysis (PCA) and linear discriminant analysis (LDA) are two important feature extraction methods and have been widely applied in a variety of areas. A limitation of PCA and LDA is that when dealing with image data, the image matrices must be first transformed into vectors, which are usually of very high dimensionality. This causes expensive computational cost and sometimes the singularity problem. Recently two methods called two-dimensional PCA (2DPCA) and two-dimensional LDA (2DLDA) were proposed to overcome this disadvantage by working directly on 2-D image matrices without a vectorization procedure. The 2DPCA and 2DLDA significantly reduce the computational effort and the possibility of singularity in feature extraction. In this paper, we show that these matrices based 2-D algorithms are equivalent to special cases of image block based feature extraction, i.e., partition each image into several blocks and perform standard PCA or LDA on the aggregate of all image blocks. These results thus provide a better understanding of the 2-D feature extraction approaches.

  1. Correlative feature analysis of FFDM images

    NASA Astrophysics Data System (ADS)

    Yuan, Yading; Giger, Maryellen L.; Li, Hui; Sennett, Charlene

    2008-03-01

    Identifying the corresponding image pair of a lesion is an essential step for combining information from different views of the lesion to improve the diagnostic ability of both radiologists and CAD systems. Because of the non-rigidity of the breasts and the 2D projective property of mammograms, this task is not trivial. In this study, we present a computerized framework that differentiates the corresponding images from different views of a lesion from non-corresponding ones. A dual-stage segmentation method, which employs an initial radial gradient index(RGI) based segmentation and an active contour model, was initially applied to extract mass lesions from the surrounding tissues. Then various lesion features were automatically extracted from each of the two views of each lesion to quantify the characteristics of margin, shape, size, texture and context of the lesion, as well as its distance to nipple. We employed a two-step method to select an effective subset of features, and combined it with a BANN to obtain a discriminant score, which yielded an estimate of the probability that the two images are of the same physical lesion. ROC analysis was used to evaluate the performance of the individual features and the selected feature subset in the task of distinguishing between corresponding and non-corresponding pairs. By using a FFDM database with 124 corresponding image pairs and 35 non-corresponding pairs, the distance feature yielded an AUC (area under the ROC curve) of 0.8 with leave-one-out evaluation by lesion, and the feature subset, which includes distance feature, lesion size and lesion contrast, yielded an AUC of 0.86. The improvement by using multiple features was statistically significant as compared to single feature performance. (p<0.001)

  2. Periosteal ganglia: CT and MR imaging features.

    PubMed

    Abdelwahab, I F; Kenan, S; Hermann, G; Klein, M J; Lewis, M M

    1993-07-01

    The imaging features of four cases of periosteal ganglia were studied. Three lesions were located over the proximal shaft of the tibia, in proximity to the pes anserinus. The fourth lesion involved the distal shaft of the ulna. Three lesions had different degrees of external cortical erosion, scalloping, and thick spicules of periosteal bone on plain radiographs. The bone adjacent to the fourth lesion was not involved. Computed tomography (CT) showed these lesions to be sharply defined soft-tissue masses abutting the periosteum. All of the lesions had the same attenuation as fluid. Magnetic resonance (MR) imaging revealed the ganglia to be sharply defined masses that were isointense compared with neighboring muscles on T1-weighted images. There was markedly increased signal intensity compared with that of fat on T2-weighted images. The signal intensity on both types of images was homogeneous. The MR imaging features were consistent with the fluid nature of the lesions. Under the appropriate clinical circumstances, the MR imaging and CT features of periosteal ganglia are diagnostic.

  3. 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.

  4. 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

  5. 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.

  6. Antimicrobial peptides with atypical structural features from the skin of the Japanese brown frog Rana japonica.

    PubMed

    Isaacson, Todd; Soto, AnaMaria; Iwamuro, Shawichi; Knoop, Floyd C; Conlon, J Michael

    2002-03-01

    Japonicin-1 (FFPIGVFCKIFKTC) and japonicin-2 (FGLPMLSILPKALCILLKRKC), two peptides with differential growth-inhibitory activity against the Gram-negative bacterium, Escherichia coli and the Gram-positive bacterium Staphylococcus aureus, were isolated from an extract of the skin of the Japanese brown frog Rana japonica. Both peptides show little amino acid sequence similarity to previously characterized antimicrobial peptides isolated from the skins of Ranid frogs. Circular dichroism studies, however, demonstrate that japonicin-2 adopts an alpha-helical conformation in 50% trifluoroethanol in common with many other cationic antimicrobial peptides synthesized in amphibian skin. Peptides belonging to the brevinin-1, brevinin-2, and tigerinin families, previously identified in the skins of Asian Ranid frogs, were not detected but a temporin-related peptide (ILPLVGNLLNDLL.NH(2); temporin-1Ja), that atypically bears no net positive charge, was isolated from the extract. The minimum inhibitory concentrations (MICs) of the peptides against E. coli were japonicin-1, 30 microM; japonicin-2, 12 microM; and temporin-1Ja > 100 microM. The MICs against S. aureus were japonicin-1, > 100 microM; japonicin-2, 20 microM; and temporin-1Ja, > 100 microM.

  7. Spinal infections: clinical and imaging features.

    PubMed

    Arbelaez, Andres; Restrepo, Feliza; Castillo, Mauricio

    2014-10-01

    Spinal infections represent a group of rare conditions affecting vertebral bodies, intervertebral discs, paraspinal soft tissues, epidural space, meninges, and spinal cord. The causal factors, clinical presentations, and imaging features are a challenge because the difficulty to differentiate them from other conditions, such as degenerative and inflammatory disorders and spinal neoplasm. They require early recognition because delay diagnosis, imaging, and intervention may have devastating consequences especially in children and the elderly. This article reviews the most common spinal infections, their pathophysiologic, clinical manifestation, and their imaging findings.

  8. Imaging features of benign adrenal cysts.

    PubMed

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

    2006-12-01

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

  9. [Chronic constrictive pericarditis: new imaging features].

    PubMed

    Pons, F; Poyet, R; Capilla, E; Brocq, F-X; Kerebel, S; Jego, C; Cellarier, G-R

    2012-11-01

    We report on a patient hospitalized in cardiology department to explore dyspnea and right ventricular failure evoking constrictive pericarditis. This case is of great interest to review conventional and new imaging features used for the diagnosis of constrictive pericarditis versus restrictive cardiomyopathy.

  10. Clinical and imaging features of fludarabine neurotoxicity.

    PubMed

    Lee, Michael S; McKinney, Alexander M; Brace, Jeffrey R; Santacruz, Karen

    2010-03-01

    Neurotoxicity from intravenous fludarabine is a rare but recognized clinical entity. Its brain imaging features have not been extensively described. Three patients received 38.5 mg or 40 mg/m per day fludarabine in a 5-day intravenous infusion before bone marrow transplantation in treatment of hematopoietic malignancies. Several weeks later, each patient developed progressive neurologic decline, including retrogeniculate blindness, leading to coma and death. Brain MRI showed progressively enlarging but mild T2/FLAIR hyperintensities in the periventricular white matter. The lesions demonstrated restricted diffusion but did not enhance. Because the neurotoxicity of fludarabine appears long after exposure, neurologic decline in this setting is likely to be attributed to opportunistic disease. However, the imaging features are distinctive in their latency and in being mild relative to the profound clinical features. The safe dose of fludarabine in this context remains controversial.

  11. 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.

  12. 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.

  13. 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

  14. Imaging Microscopic Features of Keratoconic Corneal Morphology

    PubMed Central

    Georgeon, Cristina; Andreiuolo, Felipe; Borderie, Marie; Ghoubay, Djida; Rault, Josette; Borderie, Vincent M.

    2016-01-01

    Purpose: To search for gold-standard histology indicators using alternative imaging modalities in keratoconic corneas. Methods: Prospective observational case–control study. Fourteen keratoconic corneas and 20 normal corneas (10 in vivo healthy subjects and 10 ex vivo donor corneas) were examined. Images of corneas were taken by spectral domain optical coherence tomography (SD-OCT) and in vivo confocal microscopy (IVCM) before keratoplasty. The same removed corneal buttons were imaged after keratoplasty with full-field optical coherence microscopy (FFOCM) and then fixed and sent for histology. Controls consisted of normal subjects imaged in vivo with IVCM and donor corneas imaged ex vivo with FFOCM. Corneal structural changes related to pathology were noted with each imaging modality. Cell density was quantified by manual cell counting. Results: Keratoconus indicators (ie, epithelial thinning/thickening, cell shape changes, ferritin deposits, basement membrane anomalies, Bowman layer thinning, ruptures, interruptions, scarring, stromal modifications, and appearance of Vogt striae) were generally visible with all modalities. Additional features could be seen with FFOCM in comparison with gold-standard histology, particularly in the Bowman layer region, whereas the combination of SD-OCT plus IVCM detected 76% of those features detected in histology. Three-dimensional FFOCM imaging aided interpretation of two-dimensional IVCM and SD-OCT data. Basal epithelial cell and keratocyte densities were significantly lower in patients with keratoconus than those in normals (P < 0.0001). Conclusions: Structural and cellular assessment of the keratoconic cornea by means of either in vivo SD-OCT combined with IVCM or ex vivo FFOCM in both cross-sectional and en face views can detect as many keratoconus indicators as gold-standard histology. PMID:27560027

  15. A patient with monosomy 1p36, atypical features and phenotypic similarities with Cantu syndrome.

    PubMed

    Tan, Tiong Yang; Bankier, Agnes; Slater, Howard R; Northrop, Emma L; Zacharin, Margaret; Savarirayan, Ravi

    2005-12-15

    We report on a 16-year-old boy with a distal 1p36 deletion with some clinical features consistent with Cantu syndrome (OMIM#239850). He also has hypercholesterolemia, type II diabetes, recurrent bony fractures, and non-alcoholic steatohepatitis, not previously described in either condition. The 1p36 deletion was detected in a screen of all chromosome subtelomeres using multiplex ligation-dependent probe amplification and was verified using FISH with a region-specific BAC clone. We suggest that patients suspected of having Cantu syndrome, especially those with unusual or more severe manifestations be analyzed for distal 1p36 deletions.

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

    PubMed Central

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

    2016-01-01

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

  17. Lung parenchymal invasion in pulmonary carcinoid tumor: an important histologic feature suggesting the diagnosis of atypical carcinoid and poor prognosis.

    PubMed

    Ha, Sang Yun; Lee, Jae Jun; Cho, Junhun; Hyeon, Jiyeon; Han, Joungho; Kim, Hong Kwan

    2013-05-01

    The majority of previous studies on pulmonary carcinoid tumor have usually focused on clinical behavior or outcome, seldom considering histopathologic features. We retrospectively collected 63 cases of resected pulmonary carcinoid tumors from 1995 to 2011 at Samsung Medical Center, Seoul, Korea. The clinical and pathological features were correlated and survival analyses were performed. Forty cases (63.5%) were classified as typical carcinoid (TC) and 23 cases (36.5%) were classified as atypical carcinoid (AC) according to WHO classification criteria. AC patients showed a higher frequency of current smoking status and a higher stage of the tumor by the American Joint Committee on Cancer than TC patients. The disease was associated with death and recurrence in five and seven patients, respectively, with almost all of the associations found in AC patients. The five-year survival rate of TC and AC were 100% and 83.5%, respectively, with AC showing poorer prognosis than TC in overall survival (OS) and disease free survival (DFS) (p=0.005 and p=0.002). Lung parenchymal invasion was observed more commonly in AC than in TC (39.1% vs 12.5%, p=0.01) and was a poor prognostic factor in OS and DFS. Rosette-like arrangements were found only in six cases of AC, while abundant basophilic cytoplasm mimicking paraganglioma and ossification were found only in TC. Through the comprehensive study of pulmonary carcinoid tumor in Korea, we suggest that lung parenchymal invasion could be a useful histologic feature to suspect the diagnosis of AC in daily practice as well as to predict the prognosis of carcinoid tumor.

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

    PubMed

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

    2016-09-01

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

  19. 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

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

    PubMed

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

    2015-04-01

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

  1. 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…

  2. Morphological theory in image feature extraction

    NASA Astrophysics Data System (ADS)

    Gui, Feng; Lin, QiWei

    2003-06-01

    As we know that morphology is the technique that based upon set theory and it can be used for binary image processing and gray image processing. The principle and the geometrical meaning of morphological boundary detecting for image were discussed in this paper, and the selecting of structure element was analyzed. Comparison was made between morphological boundary detecting and traditional boundary detecting method, conclusion that morphological boundary detecting method has better compatibility and anti-interference capability was reached. The method was also used for L.V. cineangiograms processing. In this paper we hoped to build up a foundation for automatic detection of L.V. contours based on the features of L.V. cineangiograms and Morphological theory, for the further study of L.V. wall motion abnormalities, because wall motion abnormalities of L.V. due to myocardia ischeamia caused by coronary atherosclerosis is a significant feature of Atherosclerotic coronary heart disease (CHD). An algorithm that based on morphology for L.V. contours extracting was developed in this paper.

  3. Targeted Imaging of the Atypical Chemokine Receptor 3 (ACKR3/CXCR7) in Human Cancer Xenografts

    PubMed Central

    Azad, Babak Behnam; Lisok, Ala; Chatterjee, Samit; Poirier, John T.; Pullambhatla, Mrudula; Luker, Gary D.; Pomper, Martin G.; Nimmagadda, Sridhar

    2017-01-01

    The atypical chemokine receptor ACKR3 (formerly CXCR7), overexpressed in various cancers compared to normal tissues, plays a pivotal role in adhesion, angiogenesis, tumorigenesis, metastasis and tumor cell survival. ACKR3 modulates the tumor microenvironment and regulates tumor growth. The therapeutic potential of ACKR3 has also been demonstrated in various murine models of human cancer. Literature findings underscore the importance of ACKR3 in disease progression and suggest it as an important diagnostic maker for non-invasive imaging of ACKR3 overexpressing malignancies. There are currently no reports on direct receptor-specific detection of ACKR3 expression. Here we report the evaluation of a radiolabeled ACKR3-targeted monoclonal antibody (ACKR3-mAb) for the non-invasive in vivo nuclear imaging of ACKR3 expression in human breast, lung and esophageal squamous cell carcinoma cancer xenografts. Methods ACKR3 transcripts were extracted from Cancer Cell Line Encyclopedia (CCLE), The Cancer Genome Atlas (TCGA) and the Clinical Lung Cancer Genome Project (CLCGP). 89Zr-ACKR3-mAb was evaluated in vitro and subsequently in vivo by positron emission tomography (PET) and ex vivo biodistribution studies in mice xenografted with breast (MDA-MB-231-ACKR3 (231-AC-KR3), MDA-MB-231 (231), MCF7), lung (HCC95) or esophageal (KYSE520) cancer cells. In addition, ACKR3-mAb was radiolabeled with Iodine-125 and evaluated by single photon emission computed tomography (SPECT) imaging and ex vivo biodistribution studies. Results ACKR3 transcript levels were highest in lung squamous cell carcinoma (LUSC) among the 21 cancer type data extracted from TCGA. Also, CLCGP data showed that LUSC has the highest CXCR7 transcript levels compared to other lung cancer subtypes. The 89Zr-ACKR3-mAb was produced in 80±5% radiochemical yields with >98% radiochemical purity. In vitro cell uptake of 89Zr-ACKR3-mAb correlated with gradient levels of cell surface ACKR3 expression observed by flow cytometry

  4. The value of Gd-EOB-DTPA-enhanced MR imaging in characterizing cirrhotic nodules with atypical enhancement on Gd-DTPA-enhanced MR images

    PubMed Central

    Lin, Ching-Po; Chen, Yao-Li; Chen, Yung-Fang; Chen, Ran-Chou

    2017-01-01

    Purpose To evaluate the utility of Gd-EOB-DTPA-enhanced magnetic resonance imaging (MRI) in characterizing atypically enhanced cirrhotic nodules detected on conventional Gd-DTPA-enhanced MR images. Materials and methods We enrolled 61 consecutive patients with 88 atypical nodules seen on conventional Gd-DTPA-enhanced MR images who underwent Gd-EOB-DTPA-enhanced MRI within a 3-month period. Using a reference standard, we determined that 58 of the nodules were hepatocellular carcinoma (HCC) and 30 were dysplastic nodules (DNs). Tumor size, signal intensity on precontrast T1-weighted images (T1WI), T2-weighted images (T2WI) and diffusion-weighted images (DWI), and the enhancement patterns seen on dynamic phase and hepatocyte phase images were determined. Results There were significant differences between DNs and HCC in hyperintensity on T2WI, hypointensity on T1WI, hypervascularity on arterial phase images, typical HCC enhancement patterns on dynamic MR images, hypointensity on hepatocyte phase images, and hyperintensity on DWI. The sensitivity and specificity were 79.3% and 83.3% for T2WI, 50.0% and 80.0% for T1WI, 82.8% and 76.7% for DWI, 17.2% and 100% for dynamic MR imaging, 93.1% and 83.3% for hepatocyte phase imaging, and 46.8% and 100% when arterial hypervascularity was combined with hypointensity on hepatocyte-phase imaging. Conclusion Gd-EOB-DTPA-enhanced hepatocyte phase imaging is recommended for patients at high risk for HCC who present with atypical lesions on conventional Gd-DTPA-enhanced MR images. PMID:28355258

  5. 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

  6. Imaging systems and applications: introduction to the feature.

    PubMed

    Imai, Francisco H; Linne von Berg, Dale C; Skauli, Torbjørn; Tominaga, Shoji; Zalevsky, Zeev

    2014-05-01

    Imaging systems have numerous applications in industrial, military, consumer, and medical settings. Assembling a complete imaging system requires the integration of optics, sensing, image processing, and display rendering. This issue features original research ranging from design of stimuli for human perception, optics applications, and image enhancement to novel imaging modalities in both color and infrared spectral imaging, gigapixel imaging as well as a systems perspective to imaging.

  7. 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

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

    PubMed

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

    2013-09-01

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

  9. 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.

  10. 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.

  11. Imaging features of extraaxial musculoskeletal tuberculosis

    PubMed Central

    Vanhoenacker, Filip M; Sanghvi, Darshana A; De Backer, Adelard I

    2009-01-01

    Tuberculosis (TB) continues to be a public health problem in both developing and industrialized countries. TB can involve pulmonary as well as extrapulmonary sites. The musculoskeletal system is involved in 1–3% of patients with tuberculosis. Although musculoskeletal TB has become uncommon in the Western world, it remains a huge problem in Asia, Africa, and many developing countries. Tuberculous spondylitis is the most common form of musculoskeletal TB and accounts for approximately 50% of cases. Extraspinal musculoskeletal TB shows a predilection for large joints (hip and knee) and para-articular areas; isolated soft tissue TB is extremely rare. Early diagnosis and prompt treatment are mandatory to prevent serious destruction of joints and skeletal deformity. However, due to the nonspecific and often indolent clinical presentation, the diagnosis may be delayed. Radiological assessment is often the first step in the diagnostic workup of patients with musculoskeletal TB and further investigations are decided by the findings on radiography. Both the radiologist and the clinician should be aware of the possibility of this diagnosis. In this manuscript we review the imaging features of extraspinal bone, joint, and soft tissue TB. PMID:19881081

  12. Lung image patch classification with automatic feature learning.

    PubMed

    Li, Qing; Cai, Weidong; Feng, David Dagan

    2013-01-01

    Image patch classification is an important task in many different medical imaging applications. The classification performance is usually highly dependent on the effectiveness of image feature vectors. While many feature descriptors have been proposed over the past years, they can be quite complicated and domain-specific. Automatic feature learning from image data has thus emerged as a different trend recently, to capture the intrinsic image features without manual feature design. In this paper, we propose to create multi-scale feature extractors based on an unsupervised learning algorithm; and obtain the image feature vectors by convolving the feature extractors with the image patches. The auto-generated image features are data-adaptive and highly descriptive. A simple classification scheme is then used to classify the image patches. The proposed method is generic in nature and can be applied to different imaging domains. For evaluation, we perform image patch classification to differentiate various lung tissue patterns commonly seen in interstitial lung disease (ILD), and demonstrate promising results.

  13. Feature-Enhanced, Model-Based Sparse Aperture Imaging

    DTIC Science & Technology

    2008-03-01

    imaging, anisotropy characterization, feature-enhanced imaging, inverse problems, superresolution , anisotropy, sparse signal representation, overcomplete...number of such activities ourselves, and we provide very brief information on some of them here. We have developed a superresolution technique for...enhanced, superresolution image reconstruction. This framework provides a number of desirable features including preservation of anisotropic scatterers

  14. Prostate Mechanical Imaging: 3-D Image Composition and Feature Calculations

    PubMed Central

    Egorov, Vladimir; Ayrapetyan, Suren; Sarvazyan, Armen P.

    2008-01-01

    We have developed a method and a device entitled prostate mechanical imager (PMI) for the real-time imaging of prostate using a transrectal probe equipped with a pressure sensor array and position tracking sensor. PMI operation is based on measurement of the stress pattern on the rectal wall when the probe is pressed against the prostate. Temporal and spatial changes in the stress pattern provide information on the elastic structure of the gland and allow two-dimensional (2-D) and three-dimensional (3-D) reconstruction of prostate anatomy and assessment of prostate mechanical properties. The data acquired allow the calculation of prostate features such as size, shape, nodularity, consistency/hardness, and mobility. The PMI prototype has been validated in laboratory experiments on prostate phantoms and in a clinical study. The results obtained on model systems and in vivo images from patients prove that PMI has potential to become a diagnostic tool that could largely supplant DRE through its higher sensitivity, quantitative record storage, ease-of-use and inherent low cost. PMID:17024836

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

    NASA Astrophysics Data System (ADS)

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

    2012-01-01

    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.

  16. 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.

  17. Prevalence, correlates, comorbidity and treatment-seeking among individuals with a lifetime major depressive episode with and without atypical features: Results from the National Epidemiologic Survey on Alcohol and Related Conditions

    PubMed Central

    Blanco, Carlos; Vesga-López, Oriana; Stewart, Jonathan W.; Liu, Shang-Min; Grant, Bridget F.; Hasin, Deborah S.

    2012-01-01

    Objective To examine prevalence, correlates, comorbidity and treatment-seeking among individuals with a lifetime major depressive episode (MDE) with and without atypical features. Methods Data were derived from the 2001–2002 National Epidemiologic Survey on Alcohol and Related Conditions, a large cross-sectional survey of a representative sample (N = 43,093) of the U.S. population, which assessed psychiatric disorders using the Alcohol Use Disorder and Associated Disabilities Interview Schedule-DSM-IV Version (AUDADIS-IV). Comparison groups were defined based on the presence or absence of hypersomnia or hyperphagia in individuals who meet criteria for lifetime DSM-IV MDE. Results The presence of atypical features during a MDE was associated with greater rates of lifetime psychiatric comorbidity, including alcohol abuse, drug dependence, dysthymia, social anxiety disorder, specific phobia and any personality disorder (PD), except antisocial PD, than MDE without atypical features. Compared with the later group, MDE with atypical features was associated with female gender, younger age of onset, more MDEs, greater episode severity and disability, higher rates of family history of depression, bipolar I disorder, suicide attempts, and larger mental health treatment-seeking rates. Conclusions Our data provide further evidence for the clinical significance and validity of this depressive specifier. Based on the presence of any of the two reversed vegetative symptoms during an MDE most of the commonly cited validators of atypical depression were confirmed in our study. MDE with atypical features may be more common, severe, and impairing than previously documented. PMID:21939615

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

    DOEpatents

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

    2014-08-19

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

  19. Introduction: feature issue on In Vivo Microcirculation Imaging.

    PubMed

    Dunn, Andrew K; Leitgeb, Rainer; Wang, Ruikang K; Zhang, Hao F

    2011-07-01

    The editors introduce the Biomedical Optics Express feature issue, "In Vivo Microcirculation Imaging," which includes 14 contributions from the biomedical optics community, covering such imaging techniques as optical coherence tomography, photoacoustic microscopy, laser Doppler /speckle imaging, and near infrared spectroscopy and fluorescence imaging.

  20. Feature-preserving image/video compression

    NASA Astrophysics Data System (ADS)

    Al-Jawad, Naseer; Jassim, Sabah

    2005-10-01

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

  1. Registration of multitemporal aerial optical images using line features

    NASA Astrophysics Data System (ADS)

    Zhao, Chenyang; Goshtasby, A. Ardeshir

    2016-07-01

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

  2. Web Image Retrieval Using Self-Organizing Feature Map.

    ERIC Educational Resources Information Center

    Wu, Qishi; Iyengar, S. Sitharama; Zhu, Mengxia

    2001-01-01

    Provides an overview of current image retrieval systems. Describes the architecture of the SOFM (Self Organizing Feature Maps) based image retrieval system, discussing the system architecture and features. Introduces the Kohonen model, and describes the implementation details of SOFM computation and its learning algorithm. Presents a test example…

  3. Iris recognition based on key image feature extraction.

    PubMed

    Ren, X; Tian, Q; Zhang, J; Wu, S; Zeng, Y

    2008-01-01

    In iris recognition, feature extraction can be influenced by factors such as illumination and contrast, and thus the features extracted may be unreliable, which can cause a high rate of false results in iris pattern recognition. In order to obtain stable features, an algorithm was proposed in this paper to extract key features of a pattern from multiple images. The proposed algorithm built an iris feature template by extracting key features and performed iris identity enrolment. Simulation results showed that the selected key features have high recognition accuracy on the CASIA Iris Set, where both contrast and illumination variance exist.

  4. 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

  5. Detecting image splicing using merged features in chroma space.

    PubMed

    Xu, Bo; 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.

  6. Diffusion Tensor Image Registration Using Hybrid Connectivity and Tensor Features

    PubMed Central

    Wang, Qian; Yap, Pew-Thian; Wu, Guorong; Shen, Dinggang

    2014-01-01

    Most existing diffusion tensor imaging (DTI) registration methods estimate structural correspondences based on voxelwise matching of tensors. The rich connectivity information that is given by DTI, however, is often neglected. In this article, we propose to integrate complementary information given by connectivity features and tensor features for improved registration accuracy. To utilize connectivity information, we place multiple anchors representing different brain anatomies in the image space, and define the connectivity features for each voxel as the geodesic distances from all anchors to the voxel under consideration. The geodesic distance, which is computed in relation to the tensor field, encapsulates information of brain connectivity. We also extract tensor features for every voxel to reflect the local statistics of tensors in its neighborhood. We then combine both connectivity features and tensor features for registration of tensor images. From the images, landmarks are selected automatically and their correspondences are determined based on their connectivity and tensor feature vectors. The deformation field that deforms one tensor image to the other is iteratively estimated and optimized according to the landmarks and their associated correspondences. Experimental results show that, by using connectivity features and tensor features simultaneously, registration accuracy is increased substantially compared with the cases using either type of features alone. PMID:24293159

  7. Learning Hierarchical Feature Extractors for Image Recognition

    DTIC Science & Technology

    2012-09-01

    3.3 Macrofeatures In convolutional neural networks (e.g., (Lee et al., 2009; Ranzato et al., 2007b)), spa- tial neighborhoods of low-level features are...function which can also be seen as a small convolutional neural network : z̃ = gk × tanh(x ∗W k) (k = 1..K). This function has been shown to produce good...import into the popular spatial pyramid framework the joint encoding of nearby features commonly practiced in neural networks , and obtain significantly

  8. Digital holography and 3D imaging: introduction to feature issue.

    PubMed

    Kim, Myung K; Hayasaki, Yoshio; Picart, Pascal; Rosen, Joseph

    2013-01-01

    This feature issue of Applied Optics on Digital Holography and 3D Imaging is the sixth of an approximately annual series. Forty-seven papers are presented, covering a wide range of topics in phase-shifting methods, low coherence methods, particle analysis, biomedical imaging, computer-generated holograms, integral imaging, and many others.

  9. 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

  10. 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

  11. Pixel-feature hybrid fusion for PET/CT images.

    PubMed

    Zhu, Yang-Ming; Nortmann, Charles A

    2011-02-01

    Color blending is a popular display method for functional and anatomic image fusion. The underlay image is typically displayed in grayscale, and the overlay image is displayed in pseudo colors. This pixel-level fusion provides too much information for reviewers to analyze quickly and effectively and clutters the display. To improve the fusion image reviewing speed and reduce the information clutter, a pixel-feature hybrid fusion method is proposed and tested for PET/CT images. Segments of the colormap are selectively masked to have a few discrete colors, and pixels displayed in the masked colors are made transparent. The colormap thus creates a false contouring effect on overlay images and allows the underlay to show through to give contours an anatomic context. The PET standardized uptake value (SUV) is used to control where colormap segments are masked. Examples show that SUV features can be extracted and blended with CT image instantaneously for viewing and diagnosis, and the non-feature part of the PET image is transparent. The proposed pixel-feature hybrid fusion highlights PET SUV features on CT images and reduces display clutters. It is easy to implement and can be used as complementarily to existing pixel-level fusion methods.

  12. Feature extraction for magnetic domain images of magneto-optical recording films using gradient feature segmentation

    NASA Astrophysics Data System (ADS)

    Quanqing, Zhu; Xinsai, Wang; Xuecheng, Zou; Haihua, Li; Xiaofei, Yang

    2002-07-01

    In this paper, we present a method to realize feature extraction on low contrast magnetic domain images of magneto-optical recording films. The method is based on the following three steps: first, Lee-filtering method is adopted to realize pre-filtering and noise reduction; this is followed by gradient feature segmentation, which separates the object area from the background area; finally the common linking method is adopted and the characteristic parameters of magnetic domain are calculated. We describe these steps with particular emphasis on the gradient feature segmentation. The results show that this method has advantages over other traditional ones for feature extraction of low contrast images.

  13. 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.

  14. Atypical methotrexate ulcerative stomatitis with features of lymphoproliferative like disorder: Report of a rare ciprofloxacin-induced case and review of the literature

    PubMed Central

    Katsoulas, Nikolaos; Piperi, Evangelia; Levidou, Georgia; Sklavounou-Andrikopoulou, Alexandra

    2016-01-01

    Methotrexate (MTX) is an established immunomodulating agent used in low doses (LDMTX) to treat several autoimmune diseases. Ulcerative stomatitis (US) may be observed as a long-term LDMTX adverse effect showing a wide histopathologic spectrum. A 73-year old female presented with painful oral ulcers of 5 days duration. The patient had been under treatment for rheumatoid arthritis with LDMTX, while one week before presentation she was prescribed ciprofloxacin for a urinary infection. Histopathologic examination of a lingual ulcer revealed a polymorphous lymphohistiocytic proliferation with scattered binucleated atypical lymphocytes. Immunohistochemically, most cells were of T-cell lineage while the EBER test was negative and a diagnosis of MTX-induced reactive ulceration was rendered. MTX cessation resulted in complete resolution of the ulcers with no recurrences reported so far. The clinical and histopathologic features of MTX-induced oral ulcers are not always diagnostic and a detailed history and an extensive clinicopathologic investigation may be needed to exclude a lymphoproliferative disorder. Key words:Atypical oral ulcers, ciprofloxacin, lymphoproliferative disorders, methotrexate. PMID:27957282

  15. Interpretation of Hydrographic Features Using Landsat Images,

    DTIC Science & Technology

    1981-06-01

    environment in the west and act and are imaged in each of the four the resulting deposition and accretion spectral channels on Landsat. The tem- of...tories, sense the earth’s surface in reflect IR radiation and will not four regions of the electromagnetic appear black. However, in the visible spectrum...Operation shows thle composite scan pattern (U.S. Geological Survey, 1976). The MSS gathers data by iciaging tile surface of the earth in four spectral

  16. Simple Low Level Features for Image Analysis

    NASA Astrophysics Data System (ADS)

    Falcoz, Paolo

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

  17. Image mosaicking using SURF features of line segments

    PubMed Central

    Shen, Dinggang; Yap, Pew-Thian

    2017-01-01

    In this paper, we present a novel image mosaicking method that is based on Speeded-Up Robust Features (SURF) of line segments, aiming to achieve robustness to incident scaling, rotation, change in illumination, and significant affine distortion between images in a panoramic series. Our method involves 1) using a SURF detection operator to locate feature points; 2) rough matching using SURF features of directed line segments constructed via the feature points; and 3) eliminating incorrectly matched pairs using RANSAC (RANdom SAmple Consensus). Experimental results confirm that our method results in high-quality panoramic mosaics that are superior to state-of-the-art methods. PMID:28296919

  18. Suggestive value of predilection site and imaging features of pediatric brainstem ganglioglioma including a case report.

    PubMed

    Anqi, X; Zhenlin, L; Xin, H; Chao, Y

    2015-02-01

    Brainstem ganglioglioma is rarely reported. Due to its low incidence and atypical site, a brainstem ganglioglioma could easily be misdiagnosed as occurs with other pathological neoplasms radiologically. Here, we report an 8-year-old girl with a brainstem tumor confirmed as a ganglioglioma based on postoperative pathology results. We suggest that when a tumor located in the lower brainstem with benign radiological characteristics occurs in a child with a long-term history, the possibility of brainstem ganglioglioma should be considered in the preoperative diagnosis in addition to other low-grade neoplasms. Early stage diagnosis of brainstem ganglioglioma based on the clinical and imaging features is valuable for clinicians in order to perform effective treatment and achieve a good prognosis.

  19. 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.

  20. 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

  1. 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

  2. Dynamic feature analysis for Voyager at the Image Processing Laboratory

    NASA Technical Reports Server (NTRS)

    Yagi, G. M.; Lorre, J. J.; Jepsen, P. L.

    1978-01-01

    Voyager 1 and 2 were launched from Cape Kennedy to Jupiter, Saturn, and beyond on September 5, 1977 and August 20, 1977. The role of the Image Processing Laboratory is to provide the Voyager Imaging Team with the necessary support to identify atmospheric features (tiepoints) for Jupiter and Saturn data, and to analyze and display them in a suitable form. This support includes the software needed to acquire and store tiepoints, the hardware needed to interactively display images and tiepoints, and the general image processing environment necessary for decalibration and enhancement of the input images. The objective is an understanding of global circulation in the atmospheres of Jupiter and Saturn. Attention is given to the Voyager imaging subsystem, the Voyager imaging science objectives, hardware, software, display monitors, a dynamic feature study, decalibration, navigation, and data base.

  3. Featured Image: Structures in the Interstellar Medium

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2017-02-01

    This beautiful false-color image (which covers 57 degrees2; click for the full view!) reveals structures in the hydrogen gas that makes up the diffuse atomic interstellar medium at intermediate latitudes in our galaxy. The imagewas created by representing three velocity channels with colors red for gas moving at 7.59 km/s, green for 5.12 km/s, and blue for 2.64 km/s and it shows the dramatically turbulent and filamentary structure of this gas. This image is one of many stunning, high-resolution observations that came out of the DRAO HI Intermediate Galactic Latitude Survey, a program that used the Synthesis Telescope at the Dominion Radio Astrophysical Observatory in British Columbia to map faint hydrogen emission at intermediate latitudes in the Milky Way. The findings from the program were recently published in a study led by Kevin Blagrave (Canadian Institute for Theoretical Astrophysics, University of Toronto); to find out more about what they learned, check out the paper below!CitationK. Blagrave et al 2017 ApJ 834 126. doi:10.3847/1538-4357/834/2/126

  4. 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.

  5. 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.

  6. Geophysical imaging of karst features in Missouri

    NASA Astrophysics Data System (ADS)

    Obi, Jeremiah Chukwunonso

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

  7. 3D ultrasound image segmentation using multiple incomplete feature sets

    NASA Astrophysics Data System (ADS)

    Fan, Liexiang; Herrington, David M.; Santago, Peter, II

    1999-05-01

    We use three features, the intensity, texture and motion to obtain robust results for segmentation of intracoronary ultrasound images. Using a parameterized equation to describe the lumen-plaque and media-adventitia boundaries, we formulate the segmentation as a parameter estimation through a cost functional based on the posterior probability, which can handle the incompleteness of the features in ultrasound images by employing outlier detection.

  8. Segmentation of MR images using multiple-feature vectors

    NASA Astrophysics Data System (ADS)

    Cole, Orlean I. B.; Daemi, Mohammad F.

    1996-04-01

    Segmentation is an important step in the analysis of MR images (MRI). Considerable progress has been made in this area, and numerous reports on 3D segmentation, volume measurement and visualization have been published in recent years. The main purpose of our study is to investigate the power and use of fractal techniques in extraction of features from MR images of the human brain. These features which are supplemented by other features are used for segmentation, and ultimately for the extraction of a known pathology, in our case multiple- sclerosis (MS) lesions. We are particularly interested in the progress of the lesions and occurrence of new lesions which in a typical case are scattered within the image and are sometimes difficult to identify visually. We propose a technique for multi-channel segmentation of MR images using multiple feature vectors. The channels are proton density, T1-weighted and T2-weighted images containing multiple-sclerosis (MS) lesions at various stages of development. We first represent each image as a set of feature vectors which are estimated using fractal techniques, and supplemented by micro-texture features and features from the gray-level co-occurrence matrix (GLCM). These feature vectors are then used in a feature selection algorithm to reduce the dimension of the feature space. The next stage is segmentation and clustering. The selected feature vectors now form the input to the segmentation and clustering routines and are used as the initial clustering parameters. For this purpose, we have used the classical K-means as the initial clustering method. The clustered image is then passed into a probabilistic classifier to further classify and validate each region, taking into account the spatial properties of the image. Initially, segmentation results were obtained using the fractal dimension features alone. Subsequently, a combination of the fractal dimension features and the supplementary features mentioned above were also obtained

  9. 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.

  10. Featured Image: The Birth of Spiral Arms

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2017-01-01

    In this figure, the top panels show three spiral galaxies in the Virgo cluster, imaged with the Sloan Digital Sky Survey. The bottom panels provide a comparison with three morphologically similar galaxies generated insimulations. The simulations run by Marcin Semczuk, Ewa okas, and Andrs del Pino (Nicolaus Copernicus Astronomical Center, Poland) were designed to examine how the spiral arms of galaxies like the Milky Way may have formed. In particular, the group exploredthe possibility that so-called grand-design spiral arms are caused by tidal effects as a Milky-Way-like galaxy orbits a cluster of galaxies. The authors show that the gravitational potential of the cluster can trigger the formation of two spiral arms each time the galaxy passes through the pericenter of its orbit around the cluster. Check out the original paper below for more information!CitationMarcin Semczuk et al 2017 ApJ 834 7. doi:10.3847/1538-4357/834/1/7

  11. Breast image feature learning with adaptive deconvolutional networks

    NASA Astrophysics Data System (ADS)

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

    2012-03-01

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

  12. Morphological Feature Extraction for Automatic Registration of Multispectral Images

    NASA Technical Reports Server (NTRS)

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

    2007-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

  14. Convolutional neural network features based change detection in satellite images

    NASA Astrophysics Data System (ADS)

    Mohammed El Amin, Arabi; Liu, Qingjie; Wang, Yunhong

    2016-07-01

    With the popular use of high resolution remote sensing (HRRS) satellite images, a huge research efforts have been placed on change detection (CD) problem. An effective feature selection method can significantly boost the final result. While hand-designed features have proven difficulties to design features that effectively capture high and mid-level representations, the recent developments in machine learning (Deep Learning) omit this problem by learning hierarchical representation in an unsupervised manner directly from data without human intervention. In this letter, we propose approaching the change detection problem from a feature learning perspective. A novel deep Convolutional Neural Networks (CNN) features based HR satellite images change detection method is proposed. The main guideline is to produce a change detection map directly from two images using a pretrained CNN. This method can omit the limited performance of hand-crafted features. Firstly, CNN features are extracted through different convolutional layers. Then, a concatenation step is evaluated after an normalization step, resulting in a unique higher dimensional feature map. Finally, a change map was computed using pixel-wise Euclidean distance. Our method has been validated on real bitemporal HRRS satellite images according to qualitative and quantitative analyses. The results obtained confirm the interest of the proposed method.

  15. 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

  16. Featured Image: Experimental Simulation of Melting Meteoroids

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2017-03-01

    Ever wonder what experimental astronomy looks like? Some days, it looks like this piece of rock in a wind tunnel (click for a betterlook!). In this photo, a piece of agrillite (a terrestrial rock) is exposed to conditions in a plasma wind tunnel as a team of scientists led by Stefan Loehle (Stuttgart University) simulate what happens to a meteoroid as it hurtles through Earths atmosphere. With these experiments, the scientists hope to better understand meteoroid ablation the process by which meteoroids are heated, melt, and evaporateas they pass through our atmosphere so that we can learn more from the meteorite fragments that make it to the ground. In the scientists experiment, the rock samples were exposed to plasma flow until they disintegrated, and this process was simultaneously studied via photography, video, high-speed imaging, thermography, and Echelle emission spectroscopy. To find out what the team learned from these experiments, you can check out the original article below.CitationStefan Loehle et al 2017 ApJ 837 112. doi:10.3847/1538-4357/aa5cb5

  17. Featured Image: Tests of an MHD Code

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2016-09-01

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

  18. Featured Image: A Looping Stellar Stream

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2016-11-01

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

  19. 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

  20. 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

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

    PubMed

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

    2016-01-29

    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.

  2. Atypical pneumonia

    MedlinePlus

    Bacteria that cause atypical pneumonia include: Mycoplasma pneumonia is caused by the bacteria Mycoplasma pneumoniae . It often affects people younger than age 40. Pneumonia due to Chlamydophila pneumoniae bacteria ...

  3. 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

  4. Automated feature extraction and classification from image sources

    USGS Publications Warehouse

    ,

    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.

  5. Sparse feature fidelity for perceptual image quality assessment.

    PubMed

    Chang, Hua-Wen; Yang, Hua; Gan, Yong; Wang, Ming-Hui

    2013-10-01

    The prediction of an image quality metric (IQM) should be consistent with subjective human evaluation. As the human visual system (HVS) is critical to visual perception, modeling of the HVS is regarded as the most suitable way to achieve perceptual quality predictions. Sparse coding that is equivalent to independent component analysis (ICA) can provide a very good description of the receptive fields of simple cells in the primary visual cortex, which is the most important part of the HVS. With this inspiration, a quality metric called sparse feature fidelity (SFF) is proposed for full-reference image quality assessment (IQA) on the basis of transformation of images into sparse representations in the primary visual cortex. The proposed method is based on the sparse features that are acquired by a feature detector, which is trained on samples of natural images by an ICA algorithm. In addition, two strategies are designed to simulate the properties of the visual perception: 1) visual attention and 2) visual threshold. The computation of SFF has two stages: training and fidelity computation, in addition, the fidelity computation consists of two components: feature similarity and luminance correlation. The feature similarity measures the structure differences between the two images, whereas the luminance correlation evaluates brightness distortions. SFF also reflects the chromatic properties of the HVS, and it is very effective for color IQA. The experimental results on five image databases show that SFF has a better performance in matching subjective ratings compared with the leading IQMs.

  6. 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

  7. Image feature extraction using Gabor-like transform

    NASA Technical Reports Server (NTRS)

    Finegan, Michael K., Jr.; Wee, William G.

    1991-01-01

    Noisy and highly textured images were operated on with a Gabor-like transform. The results were evaluated to see if useful features could be extracted using spatio-temporal operators. The use of spatio-temporal operators allows for extraction of features containing simultaneous frequency and orientation information. This method allows important features, both specific and generic, to be extracted from images. The transformation was applied to industrial inspection imagery, in particular, a NASA space shuttle main engine (SSME) system for offline health monitoring. Preliminary results are given and discussed. Edge features were extracted from one of the test images. Because of the highly textured surface (even after scan line smoothing and median filtering), the Laplacian edge operator yields many spurious edges.

  8. 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.

  9. Histopathological Image Classification using Discriminative Feature-oriented Dictionary Learning

    PubMed Central

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

    2016-01-01

    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

  10. 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.

  11. Feature preserving compression of high resolution SAR images

    NASA Astrophysics Data System (ADS)

    Yang, Zhigao; Hu, Fuxiang; Sun, Tao; Qin, Qianqing

    2006-10-01

    Compression techniques are required to transmit the large amounts of high-resolution synthetic aperture radar (SAR) image data over the available channels. Common Image compression methods may lose detail and weak information in original images, especially at smoothness areas and edges with low contrast. This is known as "smoothing effect". It becomes difficult to extract and recognize some useful image features such as points and lines. We propose a new SAR image compression algorithm that can reduce the "smoothing effect" based on adaptive wavelet packet transform and feature-preserving rate allocation. For the reason that images should be modeled as non-stationary information resources, a SAR image is partitioned to overlapped blocks. Each overlapped block is then transformed by adaptive wavelet packet according to statistical features of different blocks. In quantifying and entropy coding of wavelet coefficients, we integrate feature-preserving technique. Experiments show that quality of our algorithm up to 16:1 compression ratio is improved significantly, and more weak information is reserved.

  12. 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.

  13. High quality machine-robust image features: Identification in nonsmall cell lung cancer computed tomography images

    PubMed Central

    Hunter, Luke A.; Krafft, Shane; Stingo, Francesco; Choi, Haesun; Martel, Mary K.; Kry, Stephen F.; Court, Laurence E.

    2013-01-01

    Purpose: For nonsmall cell lung cancer (NSCLC) patients, quantitative image features extracted from computed tomography (CT) images can be used to improve tumor diagnosis, staging, and response assessment. For these findings to be clinically applied, image features need to have high intra and intermachine reproducibility. The objective of this study is to identify CT image features that are reproducible, nonredundant, and informative across multiple machines. Methods: Noncontrast-enhanced, test-retest CT image pairs were obtained from 56 NSCLC patients imaged on three CT machines from two institutions. Two machines (“M1” and “M2”) used cine 4D-CT and one machine (“M3”) used breath-hold helical 3D-CT. Gross tumor volumes (GTVs) were semiautonomously segmented then pruned by removing voxels with CT numbers less than a prescribed Hounsfield unit (HU) cutoff. Three hundred and twenty eight quantitative image features were extracted from each pruned GTV based on its geometry, intensity histogram, absolute gradient image, co-occurrence matrix, and run-length matrix. For each machine, features with concordance correlation coefficient values greater than 0.90 were considered reproducible. The Dice similarity coefficient (DSC) and the Jaccard index (JI) were used to quantify reproducible feature set agreement between machines. Multimachine reproducible feature sets were created by taking the intersection of individual machine reproducible feature sets. Redundant features were removed through hierarchical clustering based on the average correlation between features across multiple machines. Results: For all image types, GTV pruning was found to negatively affect reproducibility (reported results use no HU cutoff). The reproducible feature percentage was highest for average images (M1 = 90.5%, M2 = 94.5%, M1∩M2 = 86.3%), intermediate for end-exhale images (M1 = 75.0%, M2 = 71.0%, M1∩M2 = 52.1%), and lowest for breath-hold images (M3 = 61.0%). Between M1 and M2

  14. 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.

  15. 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.

  16. Hybrid edge and feature-based single-image superresolution

    NASA Astrophysics Data System (ADS)

    Islam, Mohammad Moinul; Islam, Mohammed Nazrul; Asari, Vijayan K.; Karim, Mohammad A.

    2016-07-01

    A neighborhood-dependent component feature learning method for regression analysis in single-image superresolution is presented. Given a low-resolution input, the method uses a directional Fourier phase feature component to adaptively learn the regression kernel based on local covariance to estimate the high-resolution image. The unique feature of the proposed method is that it uses image features to learn about the local covariance from geometric similarity between the low-resolution image and its high-resolution counterpart. For each patch in the neighborhood, we estimate four directional variances to adapt the interpolated pixels. This gives us edge information and Fourier phase gives features, which are combined to interpolate using kernel regression. In order to compare quantitatively with other state-of-the-art techniques, root-mean-square error and measure mean-square similarity are computed for the example images, and experimental results show that the proposed algorithm outperforms similar techniques available in the literature, especially at higher resolution scales.

  17. 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.

  18. Semi-Supervised Feature Transformation for Tissue Image Classification

    PubMed Central

    Watanabe, Kenji; Kobayashi, Takumi; Wada, Toshikazu

    2016-01-01

    Various systems have been proposed to support biological image analysis, with the intent of decreasing false annotations and reducing the heavy burden on biologists. These systems generally comprise a feature extraction method and a classification method. Task-oriented methods for feature extraction leverage characteristic images for each problem, and they are very effective at improving the classification accuracy. However, it is difficult to utilize such feature extraction methods for versatile task in practice, because few biologists specialize in Computer Vision and/or Pattern Recognition to design the task-oriented methods. Thus, in order to improve the usability of these supporting systems, it will be useful to develop a method that can automatically transform the image features of general propose into the effective form toward the task of their interest. In this paper, we propose a semi-supervised feature transformation method, which is formulated as a natural coupling of principal component analysis (PCA) and linear discriminant analysis (LDA) in the framework of graph-embedding. Compared with other feature transformation methods, our method showed favorable classification performance in biological image analysis. PMID:27911905

  19. Simultenious binary hash and features learning for image retrieval

    NASA Astrophysics Data System (ADS)

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

    2016-05-01

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

  20. Feature-preserving artifact removal from dermoscopy images

    NASA Astrophysics Data System (ADS)

    Zhou, Howard; Chen, Mei; Gass, Richard; Rehg, James M.; Ferris, Laura; Ho, Jonhan; Drogowski, Laura

    2008-03-01

    Dermoscopy, also called surface microscopy, is a non-invasive imaging procedure developed for early screening of skin cancer. With recent advance in skin imaging technologies and image processing techniques, there has been increasing interest in computer-aided diagnosis of skin cancer from dermoscopy images. Such diagnosis requires the identification of over one hundred cutaneous morphological features. However, computer procedures designed for extracting and classifying these intricate features can be distracted by the presence of artifacts like hair, ruler markings, and air bubbles. Therefore, reliable artifact removal is an important pre-processing step for improving the performance of computer-aided diagnosis of skin cancer. In this paper, we present a new scheme that automatically detects and removes hairs and ruler markings from dermoscopy images. Moreover, our method also addresses the issue of preserving morphological features during artifact removal. The key components of this method include explicit curvilinear structure detection and modeling, as well as feature guided exemplar-based inpainting. We experiment on a number of dermoscopy images and demonstrate that our method produces superior results compared to existing techniques.

  1. 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.

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

    PubMed

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

    2014-01-01

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

  3. Modeling neuron selectivity over simple midlevel features for image classification.

    PubMed

    Shu Kong; Zhuolin Jiang; Qiang Yang

    2015-08-01

    We now know that good mid-level features can greatly enhance the performance of image classification, but how to efficiently learn the image features is still an open question. In this paper, we present an efficient unsupervised midlevel feature learning approach (MidFea), which only involves simple operations, such as k-means clustering, convolution, pooling, vector quantization, and random projection. We show this simple feature can also achieve good performance in traditional classification task. To further boost the performance, we model the neuron selectivity (NS) principle by building an additional layer over the midlevel features prior to the classifier. The NS-layer learns category-specific neurons in a supervised manner with both bottom-up inference and top-down analysis, and thus supports fast inference for a query image. Through extensive experiments, we demonstrate that this higher level NS-layer notably improves the classification accuracy with our simple MidFea, achieving comparable performances for face recognition, gender classification, age estimation, and object categorization. In particular, our approach runs faster in inference by an order of magnitude than sparse coding-based feature learning methods. As a conclusion, we argue that not only do carefully learned features (MidFea) bring improved performance, but also a sophisticated mechanism (NS-layer) at higher level boosts the performance further.

  4. 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.

  5. 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.

  6. 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.

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

    PubMed

    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

    2012-02-01

    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.

  8. Small round blue cell sarcoma of bone mimicking atypical Ewing's sarcoma with neuroectodermal features. An analysis of five cases with immunohistochemical and electron microscopic support.

    PubMed

    Llombart-Bosch, A; Lacombe, M J; Contesso, G; Peydro-Olaya, A

    1987-10-01

    Ewing's sarcoma (ES) of bone may occasionally display rosette-like textures mimicking Homer-Wright ones, as seen in neuroectodermic neoplasms (neuroblastoma, peripheral neuroepithelioma). Of a group of 39 cases of ES, reviewed with electron microscopic study, the authors have isolated five atypical ES, which histologically also possessed neuroectodermic traces. These tumors were composed of small round blue cells with rosette-like figures and cytoplasmic glycogen. The immunohistochemical analysis showed positivity for neuron-specific enolase (NSE) as well as for HNK-1 (leu-7) monoclonal antibody. Electron microscopic examination confirmed the tumor cell as being of small round type, with a dense chromatine pattern and the presence of isolated dendritic processes, as well as synaptic-like buttons; intermediate filaments, neurotubuli, and dense-core neurosecretory granules also were seen. Moreover, in two cases basement-like condensations surrounded some cells. Scanning electron microscopic study in one case confirmed the presence of rosette-like figures and cell elongations with short dendritic projections of the cytoplasm. Clinically and radiologically these cases showed features similar to ES of bone; one case, located in the chest wall, had a local relapse after treatment, with the histologic features of a pleomorphic neuroblastoma. The authors conclude that these tumors resemble closely immature neuroepithelioma of soft tissue but, being primary to bone, are superimposable on those described as "neuroectodermal tumors of bone."

  9. Feature extraction with LIDAR data and aerial images

    NASA Astrophysics Data System (ADS)

    Mao, Jianhua; Liu, Yanjing; Cheng, Penggen; Li, Xianhua; Zeng, Qihong; Xia, Jing

    2006-10-01

    Raw LIDAR data is a irregular spacing 3D point cloud including reflections from bare ground, buildings, vegetation and vehicles etc., and the first task of the data analyses of point cloud is feature extraction. However, the interpretability of LIDAR point cloud is often limited due to the fact that no object information is provided, and the complex earth topography and object morphology make it impossible for a single operator to classify all the point cloud precisely 100%. In this paper, a hierarchy method for feature extraction with LIDAR data and aerial images is discussed. The aerial images provide us information of objects figuration and spatial distribution, and hierarchic classification of features makes it easy to apply automatic filters progressively. And the experiment results show that, using this method, it was possible to detect more object information and get a better result of feature extraction than using automatic filters alone.

  10. Imaging features of complex sclerosing lesions of the breast

    PubMed Central

    2014-01-01

    Purpose: The purpose of this study was to evaluate the imaging features of complex sclerosing lesions of the breast and to assess the rate of upgrade to breast cancer. Methods: From March 2008 to May 2012, seven lesions were confirmed as complex sclerosing lesions by ultrasonography-guided core needle biopsy. Final results by either surgical excision or follow-up imaging studies were reviewed to assess the rate of upgrade to breast cancer. Two radiologists retrospectively analyzed the imaging findings according to the Breast Imaging Reporting and Data System classification. Results: Five lesions underwent subsequent surgical excision and two of them revealed ductal carcinoma in situ (n=1) and invasive ductal carcinoma (n=1). Our study showed a breast cancer upgrade rate of 28.6% (2 of 7 lesions). Two lesions were stable on imaging follow-up beyond 1 year. The mammographic features included masses (n=4, 57.1%), architectural distortion (n=2, 28.6%), and focal asymmetry (n=1, 14.3%). Common B-mode ultrasonographic features were irregular shape (n=6, 85.7%), spiculated margin (n=5, 71.4 %), and hypoechogenicity (n=7, 100%). The final assessment categories were category 4 (n=6, 85.7%) and category 5 (n=1, 14.3%). Conclusion: The complex sclerosing lesions were commonly mass-like on mammography and showed the suspicious ultrasonographic features of category 4. Due to a high underestimation rate, all complex sclerosing lesions by core needle biopsy should be excised. PMID:24936496

  11. Estimating signal features from noisy images with stochastic backgrounds

    NASA Astrophysics Data System (ADS)

    Whitaker, Meredith Kathryn

    Imaging is often used in scientific applications as a measurement tool. The location of a target, brightness of a star, and size of a tumor are all examples of object features that are sought after in various imaging applications. A perfect measurement of these quantities from image data is impossible because of, most notably, detector noise fluctuations, finite resolution, sensitivity of the imaging instrument, and obscuration by undesirable object structures. For these reasons, sophisticated image-processing techniques are designed to treat images as random variables. Quantities calculated from an image are subject to error and fluctuation; implied by calling them estimates of object features. This research focuses on estimator error for tasks common to imaging applications. Computer simulations of imaging systems are employed to compare the estimates to the true values. These computations allow for algorithm performance tests and subsequent development. Estimating the location, size, and strength of a signal embedded in a background structure from noisy image data is the basic task of interest. The estimation task's degree of difficulty is adjusted to discover the simplest data-processing necessary to yield successful estimates. Even when using an idealized imaging model, linear Wiener estimation was found to be insufficient for estimating signal location and shape. These results motivated the investigation of more complex data processing. A new method (named the scanning-linear estimator because it maximizes a linear functional) is successful in cases where linear estimation fails. This method has also demonstrated positive results when tested in realistic simulations of tomographic SPECT imaging systems. A comparison to a model of current clinical estimation practices found that the scanning-linear method offers substantial gains in performance.

  12. 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.

  13. Knowledge-based topographic feature extraction in medical images

    NASA Astrophysics Data System (ADS)

    Qian, JianZhong; Khair, Mohammad M.

    1995-08-01

    Diagnostic medical imaging often contains variations of patient anatomies, camera mispositioning, or other imperfect imaging condiitons. These variations contribute to uncertainty about shapes and boundaries of objects in images. As the results sometimes image features, such as traditional edges, may not be identified reliably and completely. We describe a knowledge based system that is able to reason about such uncertainties and use partial and locally ambiguous information to infer about shapes and lcoation of objects in an image. The system uses directional topographic features (DTFS), such as ridges and valleys, labeled from the underlying intensity surface to correlate to the intrinsic anatomical information. By using domain specific knowledge, the reasoning system can deduce significant anatomical landmarks based upon these DTFS, and can cope with uncertainties and fill in missing information. A succession of levels of representation for visual information and an active process of uncertain reasoning about this visual information are employed to realiably achieve the goal of image analysis. These landmarks can then be used in localization of anatomy of interest, image registration, or other clinical processing. The successful application of this system to a large set of planar cardiac images of nuclear medicine studies has demonstrated its efficiency and accuracy.

  14. Feature statistic analysis of ultrasound images of liver cancer

    NASA Astrophysics Data System (ADS)

    Huang, Shuqin; Ding, Mingyue; Zhang, Songgeng

    2007-12-01

    In this paper, a specific feature analysis of liver ultrasound images including normal liver, liver cancer especially hepatocellular carcinoma (HCC) and other hepatopathy is discussed. According to the classification of hepatocellular carcinoma (HCC), primary carcinoma is divided into four types. 15 features from single gray-level statistic, gray-level co-occurrence matrix (GLCM), and gray-level run-length matrix (GLRLM) are extracted. Experiments for the discrimination of each type of HCC, normal liver, fatty liver, angioma and hepatic abscess have been conducted. Corresponding features to potentially discriminate them are found.

  15. 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.

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

    PubMed Central

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

    2014-01-01

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

  17. 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...

  18. 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

  19. Moment feature based fast feature extraction algorithm for moving object detection using aerial images.

    PubMed

    Saif, A F M Saifuddin; Prabuwono, Anton Satria; Mahayuddin, Zainal Rasyid

    2015-01-01

    Fast and computationally less complex feature extraction for moving object detection using aerial images from unmanned aerial vehicles (UAVs) remains as an elusive goal in the field of computer vision research. The types of features used in current studies concerning moving object detection are typically chosen based on improving detection rate rather than on providing fast and computationally less complex feature extraction methods. Because moving object detection using aerial images from UAVs involves motion as seen from a certain altitude, effective and fast feature extraction is a vital issue for optimum detection performance. This research proposes a two-layer bucket approach based on a new feature extraction algorithm referred to as the moment-based feature extraction algorithm (MFEA). Because a moment represents the coherent intensity of pixels and motion estimation is a motion pixel intensity measurement, this research used this relation to develop the proposed algorithm. The experimental results reveal the successful performance of the proposed MFEA algorithm and the proposed methodology.

  20. No-reference face image assessment based on deep features

    NASA Astrophysics Data System (ADS)

    Liu, Guirong; Xu, Yi; Lan, Jinpeng

    2016-09-01

    Face quality assessment is important to improve the performance of face recognition system. For instance, it is required to select images of good quality to improve recognition rate for the person of interest. Current methods mostly depend on traditional image assessment, which use prior knowledge of human vision system. As a result, the quality score of face images shows consistency with human vision perception but deviates from the processing procedure of a real face recognition system. It is the fact that the state-of-art face recognition systems are all built on deep neural networks. Naturally, it is expected to propose an efficient quality scoring method of face images, which should show high consistency with the recognition rate of face images from current face recognition systems. This paper proposes a non-reference face image assessment algorithm based on the deep features, which is capable of predicting the recognition rate of face images. The proposed face image assessment algorithm provides a promising tool to filter out the good input images for the real face recognition system to achieve high recognition rate.

  1. Digital image comparison using feature extraction and luminance matching

    NASA Astrophysics Data System (ADS)

    Bachnak, Ray A.; Steidley, Carl W.; Funtanilla, Jeng

    2005-03-01

    This paper presents the results of comparing two digital images acquired using two different light sources. One of the sources is a 50-W metal halide lamp located in the compartment of an industrial borescope and the other is a 1 W LED placed at the tip of the insertion tube of the borescope. The two images are compared quantitatively and qualitatively using feature extraction and luminance matching approaches. Quantitative methods included the images' histograms, intensity profiles along a line segment, edges, and luminance measurement. Qualitative methods included image registration and linear conformal transformation with eight control points. This transformation is useful when shapes in the input image are unchanged, but the image is distorted by some combination of translation, rotation, and scaling. The gray-level histogram, edge detection, image profile and image registration do not offer conclusive results. The LED light source, however, produces good images for visual inspection by the operator. The paper presents the results and discusses the usefulness and shortcomings of various comparison methods.

  2. Rest myocardial perfusion imaging in a patient with atypical chest pain and a nondiagnostic electrocardiogram.

    PubMed

    Grube, Heinrich; Rosenblatt, Jeffrey

    2010-02-01

    ACC/AHA guidelines assign a class I indication for use of myocardial perfusion imaging (MPI) for the evaluation of chest pain in patients with acute coronary syndromes and a nondiagnostic ECG. However, MPI is not a widely used modality for the evaluation of patients who present to the ER with chest pain and an intermediate pretest probability for coronary artery disease.We report a case in which resting MPI was pivotal in diagnosing acute myocardial infarction and expedited the appropriate reperfusion strategy.

  3. Weighted feature fusion for content-based image retrieval

    NASA Astrophysics Data System (ADS)

    Soysal, Omurhan A.; Sumer, Emre

    2016-07-01

    The feature descriptors such as SIFT (Scale Invariant Feature Transform), SURF (Speeded-up Robust Features) and ORB (Oriented FAST and Rotated BRIEF) are known as the most commonly used solutions for the content-based image retrieval problems. In this paper, a novel approach called "Weighted Feature Fusion" is proposed as a generic solution instead of applying problem-specific descriptors alone. Experiments were performed on two basic data sets of the Inria in order to improve the precision of retrieval results. It was found that in cases where the descriptors were used alone the proposed approach yielded 10-30% more accurate results than the ORB alone. Besides, it yielded 9-22% and 12-29% less False Positives compared to the SIFT alone and SURF alone, respectively.

  4. Atypical presentations of atypical antipsychotics.

    PubMed

    Lind, Cpt Christopher K; Carchedi, Cpt Lisa R; Staudenmeier, Ltc James J; Diebold, Ltc P Carroll J

    2005-06-01

    The atypical antipsychotics have been touted by many as having minimal extrapyramidal symptoms. This case series from the Tripler Army Medical Center Psychiatry Graduate Medical Education Program presents the extrapyramidal symptoms observed with four different atypical antipsychotic medications. Also reviewed are the mechanisms of action that atypical antipsychotics and first-generation antipsychotics use to treat the symptoms of schizophrenia. Cases reviewed include a schizophrenic male patient whose dose of risperidone was doubled from 6mg to 12mg overnight and developed an acute dystonic reaction; a young male patient with a substance-induced psychosis who unintentionally doubled his ziprasidone dose in 24 hours, resulting in an acute dystonic reaction; a young female patient on paroxetine who also recently started olanzapine and had complaints consistent with akathisia that resolved with treatment; and an adolescent female patient on escitalopram for obsessive-compulsive disorder who after starting aripiprazole developed Parkinsonism. All four cases illustrate that even though atypical antipsychotics are less likely to cause extrapyramidal symptoms than their first generation cousins, the physician should be aware that these symptoms may still occur and need to be treated.

  5. Atypical Presentations of Atypical Antipsychotics

    PubMed Central

    Carchedi, CPT. Lisa R.; Staudenmeier, LTC. James J.; Diebold, LTC(P). Carroll J.

    2005-01-01

    The atypical antipsychotics have been touted by many as having minimal extrapyramidal symptoms. This case series from the Tripler Army Medical Center Psychiatry Graduate Medical Education Program presents the extrapyramidal symptoms observed with four different atypical antipsychotic medications. Also reviewed are the mechanisms of action that atypical antipsychotics and first-generation antipsychotics use to treat the symptoms of schizophrenia. Cases reviewed include a schizophrenic male patient whose dose of risperidone was doubled from 6mg to 12mg overnight and developed an acute dystonic reaction; a young male patient with a substance-induced psychosis who unintentionally doubled his ziprasidone dose in 24 hours, resulting in an acute dystonic reaction; a young female patient on paroxetine who also recently started olanzapine and had complaints consistent with akathisia that resolved with treatment; and an adolescent female patient on escitalopram for obsessive-compulsive disorder who after starting aripiprazole developed Parkinsonism. All four cases illustrate that even though atypical antipsychotics are less likely to cause extrapyramidal symptoms than their first generation cousins, the physician should be aware that these symptoms may still occur and need to be treated. PMID:21152153

  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. 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.

  8. 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.

  9. Adaptive feature-specific imaging: a face recognition example.

    PubMed

    Baheti, Pawan K; Neifeld, Mark A

    2008-04-01

    We 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. Using sequential hypothesis testing, we compare AFSI with static-FSI (SFSI) and static or adaptive conventional imaging in terms of the number of measurements required to achieve a specified probability of misclassification (Pe). The AFSI system exhibits significant improvement compared to SFSI and conventional imaging at low signal-to-noise ratio (SNR). It is shown that for M=4 hypotheses and desired Pe=10(-2), AFSI requires 100 times fewer measurements than the adaptive conventional imager at SNR= -20 dB. We also show a trade-off, in terms of average detection time, between measurement SNR and adaptation advantage, resulting in an optimal value of integration time (equivalent to SNR) per measurement.

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

    NASA Astrophysics Data System (ADS)

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

    2003-02-01

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

  11. Imaging features of an intraosseous arteriovenous malformation in the tibia

    PubMed Central

    Wang, Hong-Hau; Yeh, Tsu-Te; Lin, Yu-Chun; Huang, Guo-Shu

    2015-01-01

    Primary intraosseous arteriovenous malformations (AVMs) are rare and have only been occasionally reported. We herein report a histologically proven case of primary intraosseous AVM in the tibia, which mimicked a fibrous tumour on radiography. This presentation carries a risk of triggering acute large haemorrhage through unnecessary biopsy. In intraosseous AVM, the magnetic resonance (MR) imaging features typical of a soft tissue AVM are absent, making diagnosis difficult. In this report, peculiar MR features in the presence of a connecting vessel between the normal deep venous system of the lower extremity and the tumour provide a clue for the early diagnosis of primary intraosseous AVM. PMID:25715860

  12. Feature Preserving Image Smoothing Using a Continuous Mixture of Tensors *

    PubMed Central

    Subakan, Özlem; Jian, Bing; Vemuri, Baba C.; Vallejos, C. Eduardo

    2009-01-01

    Many computer vision and image processing tasks require the preservation of local discontinuities, terminations and bifurcations. Denoising with feature preservation is a challenging task and in this paper, we present a novel technique for preserving complex oriented structures such as junctions and corners present in images. This is achieved in a two stage process namely, (1) All image data are pre-processed to extract local orientation information using a steerable Gabor filter bank. The orientation distribution at each lattice point is then represented by a continuous mixture of Gaussians. The continuous mixture representation can be cast as the Laplace transform of the mixing density over the space of positive definite (covariance) matrices. This mixing density is assumed to be a parameterized distribution, namely, a mixture of Wisharts whose Laplace transform is evaluated in a closed form expression called the Rigaut type function, a scalar-valued function of the parameters of the Wishart distribution. Computation of the weights in the mixture Wisharts is formulated as a sparse deconvolution problem. (2) The feature preserving denoising is then achieved via iterative convolution of the given image data with the Rigaut type function. We present experimental results on noisy data, real 2D images and 3D MRI data acquired from plant roots depicting bifurcating roots. Superior performance of our technique is depicted via comparison to the state-of-the-art anisotropic diffusion filter. PMID:20072714

  13. Imaging features of intraosseous ganglia: a report of 45 cases.

    PubMed

    Williams, H J; Davies, A M; Allen, G; Evans, N; Mangham, D C

    2004-10-01

    The aim of this study is to report the spectrum of imaging findings of intraosseous ganglia (IG) with particular emphasis on the radiographic and magnetic resonance (MR) features. Forty-five patients with a final diagnosis of IG were referred to a specialist orthopaedic oncology service with the presumptive diagnosis of either a primary or secondary bone tumour. The diagnosis was established by histology in 25 cases. In the remainder, the imaging features were considered characteristic and the lesion was stable on follow-up radiographic examination. Radiographs were available for retrospective review in all cases and MR imaging in 29. There was a minor male preponderance with a wide adult age range. Three quarters were found in relation to the weight-bearing long bones of the lower limb, particularly round the knee. On radiographs all were juxta-articular and osteolytic; 74% were eccentric in location, 80% had a sclerotic endosteal margin and 60% of cases showed a degree of trabeculation. Periosteal new bone formation and matrix mineralization were not present. Of the 29 cases that underwent MR imaging, 66% were multiloculated. On T1-weighted images the IG contents were isointense or mildly hypointense in 90% cases. Forty-one per cent of the cases showed a slightly hyperintense rim lining that enhanced with a gadolinium chelate. Thirty-eight per cent were associated with soft tissue extension and 17% with a defect of the adjacent articular cortex. Fifty-five per cent showed surrounding marrow oedema on T2-weighted or STIR images and two cases (7%) a fluid-fluid level prior to any surgical intervention. The authors contend that it is semantics to differentiate between an IG and a degenerate subchondral cyst as, while the initial pathogenesis may vary, the histological endpoint is identical, as are the imaging features apart from the degree of associated degenerative joint disease. IGs, particularly when large, may be mistaken for a bone tumour. Correlation of the

  14. Level set method coupled with Energy Image features for brain MR image segmentation.

    PubMed

    Punga, Mirela Visan; Gaurav, Rahul; Moraru, Luminita

    2014-06-01

    Up until now, the noise and intensity inhomogeneity are considered one of the major drawbacks in the field of brain magnetic resonance (MR) image segmentation. This paper introduces the energy image feature approach for intensity inhomogeneity correction. Our approach of segmentation takes the advantage of image features and preserves the advantages of the level set methods in region-based active contours framework. The energy image feature represents a new image obtained from the original image when the pixels' values are replaced by local energy values computed in the 3×3 mask size. The performance and utility of the energy image features were tested and compared through two different variants of level set methods: one as the encompassed local and global intensity fitting method and the other as the selective binary and Gaussian filtering regularized level set method. The reported results demonstrate the flexibility of the energy image feature to adapt to level set segmentation framework and to perform the challenging task of brain lesion segmentation in a rather robust way.

  15. Hyperspectral image classification based on NMF Features Selection Method

    NASA Astrophysics Data System (ADS)

    Abe, Bolanle T.; Jordaan, J. A.

    2013-12-01

    Hyperspectral instruments are capable of collecting hundreds of images corresponding to wavelength channels for the same area on the earth surface. Due to the huge number of features (bands) in hyperspectral imagery, land cover classification procedures are computationally expensive and pose a problem known as the curse of dimensionality. In addition, higher correlation among contiguous bands increases the redundancy within the bands. Hence, dimension reduction of hyperspectral data is very crucial so as to obtain good classification accuracy results. This paper presents a new feature selection technique. Non-negative Matrix Factorization (NMF) algorithm is proposed to obtain reduced relevant features in the input domain of each class label. This aimed to reduce classification error and dimensionality of classification challenges. Indiana pines of the Northwest Indiana dataset is used to evaluate the performance of the proposed method through experiments of features selection and classification. The Waikato Environment for Knowledge Analysis (WEKA) data mining framework is selected as a tool to implement the classification using Support Vector Machines and Neural Network. The selected features subsets are subjected to land cover classification to investigate the performance of the classifiers and how the features size affects classification accuracy. Results obtained shows that performances of the classifiers are significant. The study makes a positive contribution to the problems of hyperspectral imagery by exploring NMF, SVMs and NN to improve classification accuracy. The performances of the classifiers are valuable for decision maker to consider tradeoffs in method accuracy versus method complexity.

  16. Photoacoustic imaging features of intraocular tumors: Retinoblastoma and uveal melanoma

    PubMed Central

    Xu, Guan; Xue, Yafang; Özkurt, Zeynep Gürsel; Slimani, Naziha; Hu, Zizhong; Wang, Xueding; Xia, Kewen; Ma, Teng; Zhou, Qifa; Demirci, Hakan

    2017-01-01

    The purpose of this study is to examine the capability of photoacoustic (PA) imaging (PAI) in assessing the unique molecular and architectural features in ocular tumors. A real-time PA and ultrasonography (US) parallel imaging system based on a research US platform was developed to examine retinoblastoma in mice in vivo and human retinoblastoma and uveal melanoma ex vivo. PA signals were generated by optical illumination at 720, 750, 800, 850, 900 and 950 nm delivered through a fiber optical bundle. The optical absorption spectra of the tumors were derived from the PA images. The optical absorption spectrum of each tumor was quantified by fitting to a polynomial model. The microscopic architectures of the tumors were quantified by frequency domain analysis of the PA signals. Both the optical spectral and architectural features agree with the histological findings of the tumors. The mouse and human retinoblastoma showed comparable total optical absorption spectra at a correlation of 0.95 (p<0.005). The quantitative PAI features of human retinoblastoma and uveal melanoma have shown statistically significant difference in two tailed t-tests (p<0.05). Fully compatible with the concurrent procedures, PAI could be a potential tool complementary to other diagnostic modalities for characterizing intraocular tumors. PMID:28231293

  17. 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

  18. Medical Image Fusion Based on Feature Extraction and Sparse Representation

    PubMed Central

    Wei, Gao; Zongxi, Song

    2017-01-01

    As a novel multiscale geometric analysis tool, sparse representation has shown many advantages over the conventional image representation methods. However, the standard sparse representation does not take intrinsic structure and its time complexity into consideration. In this paper, a new fusion mechanism for multimodal medical images based on sparse representation and decision map is proposed to deal with these problems simultaneously. Three decision maps are designed including structure information map (SM) and energy information map (EM) as well as structure and energy map (SEM) to make the results reserve more energy and edge information. SM contains the local structure feature captured by the Laplacian of a Gaussian (LOG) and EM contains the energy and energy distribution feature detected by the mean square deviation. The decision map is added to the normal sparse representation based method to improve the speed of the algorithm. Proposed approach also improves the quality of the fused results by enhancing the contrast and reserving more structure and energy information from the source images. The experiment results of 36 groups of CT/MR, MR-T1/MR-T2, and CT/PET images demonstrate that the method based on SR and SEM outperforms five state-of-the-art methods. PMID:28321246

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

    PubMed

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

    2016-09-01

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

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

    PubMed

    Dykes, Michael I; Vijay, Ram K P

    2014-02-01

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

  1. 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.

  2. 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.

  3. Enhancing automatic classification of hepatocellular carcinoma images through image masking, tissue changes and trabecular features

    PubMed Central

    Aziz, Maulana Abdul; Kanazawa, Hiroshi; Murakami, Yuri; Kimura, Fumikazu; Yamaguchi, Masahiro; Kiyuna, Tomoharu; Yamashita, Yoshiko; Saito, Akira; Ishikawa, Masahiro; Kobayashi, Naoki; Abe, Tokiya; Hashiguchi, Akinori; Sakamoto, Michiie

    2015-01-01

    Background: Recent breakthroughs in computer vision and digital microscopy have prompted the application of such technologies in cancer diagnosis, especially in histopathological image analysis. Earlier, an attempt to classify hepatocellular carcinoma images based on nuclear and structural features has been carried out on a set of surgical resected samples. Here, we proposed methods to enhance the process and improve the classification performance. Methods: First, we segmented the histological components of the liver tissues and generated several masked images. By utilizing the masked images, some set of new features were introduced, producing three sets of features consisting nuclei, trabecular and tissue changes features. Furthermore, we extended the classification process by using biopsy resected samples in addition to the surgical samples. Results: Experiments by using support vector machine (SVM) classifier with combinations of features and sample types showed that the proposed methods improve the classification rate in HCC detection for about 1-3%. Moreover, detection rate of low-grades cancer increased when the new features were appended in the classification process, although the rate was worsen in the case of undifferentiated tumors. Conclusions: The masking process increased the reliability of extracted nuclei features. The additional of new features improved the system especially for early HCC detection. Likewise, the combination of surgical and biopsy samples as training data could also improve the classification rates. Therefore, the methods will extend the support for pathologists in the HCC diagnosis. PMID:26110093

  4. 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

  5. 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.

  6. 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

  7. Optimum imaging time selection algorithm for inverse synthetic aperture radar images using geometric features and image gradient

    NASA Astrophysics Data System (ADS)

    Fulin, Su; Hongxin, Yang

    2016-07-01

    For better using of inverse synthetic aperture radar (ISAR) images of ship targets, it is more desirable to select a proper imaging time to obtain high quality top-view or side-view images. However, optimum imaging time selection is not robust enough for the restriction of traditional geometric feature extraction methods. In our study, we propose a method based on the geometric features and gradient maximization. First, we select the imaging instant from radar echoes by the centerline and mainmast of the ship. In this part, we propose a geometric features extraction method to improve the robustness of instant selection in different scenarios. Then, an image gradient maximization is employed to estimate the period for ISAR imaging. Finally, experimental results of both simulated and real signals are provided to demonstrate the effectiveness and practicability of the algorithm.

  8. Ice images processing interface for automatic features extraction

    NASA Astrophysics Data System (ADS)

    Tardif, Pierre M.

    2001-02-01

    Canadian Coast Guard has the mandate to maintain the navigability of the St.-Lawrence seaway. It must prevent ice jam formation. Radar, sonar sensors and cameras are used to verify ice movement and keep a record of pertinent data. The cameras are placed along the seaway at strategic locations. Images are processed and saved for future reference. The Ice Images Processing Interface (IIPI) is an integral part of Ices Integrated System (IIS). This software processes images to extract the ice speed, concentration, roughness, and rate of flow. Ice concentration is computed from image segmentation using color models and a priori information. Speed is obtained from a region-matching algorithm. Both concentration and speed calculations are complex, since they require a calibration step involving on-site measurements. Color texture features provide ice roughness estimation. Rate of flow uses ice thickness, which is estimated from sonar sensors on the river floor. Our paper will present how we modeled and designed the IIPI, the issues involved and its future. For more reliable results, we suggest that meteorological data be provided, change in camera orientation be changed, sun reflections be anticipated, and more a priori information, such as radar images available at some sites, be included.

  9. A novel point cloud registration using 2D image features

    NASA Astrophysics Data System (ADS)

    Lin, Chien-Chou; Tai, Yen-Chou; Lee, Jhong-Jin; Chen, Yong-Sheng

    2017-01-01

    Since a 3D scanner only captures a scene of a 3D object at a time, a 3D registration for multi-scene is the key issue of 3D modeling. This paper presents a novel and an efficient 3D registration method based on 2D local feature matching. The proposed method transforms the point clouds into 2D bearing angle images and then uses the 2D feature based matching method, SURF, to find matching pixel pairs between two images. The corresponding points of 3D point clouds can be obtained by those pixel pairs. Since the corresponding pairs are sorted by their distance between matching features, only the top half of the corresponding pairs are used to find the optimal rotation matrix by the least squares approximation. In this paper, the optimal rotation matrix is derived by orthogonal Procrustes method (SVD-based approach). Therefore, the 3D model of an object can be reconstructed by aligning those point clouds with the optimal transformation matrix. Experimental results show that the accuracy of the proposed method is close to the ICP, but the computation cost is reduced significantly. The performance is six times faster than the generalized-ICP algorithm. Furthermore, while the ICP requires high alignment similarity of two scenes, the proposed method is robust to a larger difference of viewing angle.

  10. 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.

  11. 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.

  12. Sparse coding based feature representation method for remote sensing images

    NASA Astrophysics Data System (ADS)

    Oguslu, Ender

    In this dissertation, we study sparse coding based feature representation method for the classification of multispectral and hyperspectral images (HSI). The existing feature representation systems based on the sparse signal model are computationally expensive, requiring to solve a convex optimization problem to learn a dictionary. A sparse coding feature representation framework for the classification of HSI is presented that alleviates the complexity of sparse coding through sub-band construction, dictionary learning, and encoding steps. In the framework, we construct the dictionary based upon the extracted sub-bands from the spectral representation of a pixel. In the encoding step, we utilize a soft threshold function to obtain sparse feature representations for HSI. Experimental results showed that a randomly selected dictionary could be as effective as a dictionary learned from optimization. The new representation usually has a very high dimensionality requiring a lot of computational resources. In addition, the spatial information of the HSI data has not been included in the representation. Thus, we modify the framework by incorporating the spatial information of the HSI pixels and reducing the dimension of the new sparse representations. The enhanced model, called sparse coding based dense feature representation (SC-DFR), is integrated with a linear support vector machine (SVM) and a composite kernels SVM (CKSVM) classifiers to discriminate different types of land cover. We evaluated the proposed algorithm on three well known HSI datasets and compared our method to four recently developed classification methods: SVM, CKSVM, simultaneous orthogonal matching pursuit (SOMP) and image fusion and recursive filtering (IFRF). The results from the experiments showed that the proposed method can achieve better overall and average classification accuracies with a much more compact representation leading to more efficient sparse models for HSI classification. To further

  13. 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

  14. Diagnostic role of (99)Tc(m)-MDP SPECT/CT combined SPECT/MRI Multi modality imaging for early and atypical bone metastases.

    PubMed

    Chen, Xiao-Liang; Li, Qian; Cao, Lin; Jiang, Shi-Xi

    2014-01-01

    The bone metastasis appeared early before the bone imaging for most of the above patients. (99)Tc(m)-MDP ((99)Tc(m) marked methylene diphosphonate) bone imaging could diagnosis the bone metastasis with highly sensitivity, but with lower specificity. The aim of this study is to explore the diagnostic value of (99)Tc(m)-MDP SPECT/CT combined SPECT/MRI Multi modality imaging for the early period atypical bone metastases. 15 to 30 mCi (99)Tc(m)-MDP was intravenously injected to the 34 malignant patients diagnosed as doubtful early bone metastases. SPECT, CT and SPECT/CT images were captured and analyzed consequently. For the patients diagnosed as early period atypical bone metastases by SPECT/CT, combining the SPECT/CT and MRI together as the SPECT/MRI integrated image. The obtained SPECT/MRI image was analyzed and compared with the pathogenic results of patients. The results indicated that 34 early period doubtful metastatic focus, including 34 SPECT positive focus, 17 focus without special changes by using CT method, 11 bone metastases focus by using SPECT/CT method, 23 doubtful bone metastases focus, 8 doubtful bone metastases focus, 14 doubtful bone metastases focus and 2 focus without clear image. Totally, SPECT/CT combined with SPECT/MRI method diagnosed 30 bone metastatic focus and 4 doubtfully metastatic focus. In conclusion, (99)Tc(m)-MDP SPECT/CT combined SPECT/MRI Multi modality imaging shows a higher diagnostic value for the early period bone metastases, which also enhances the diagnostic accuracy rate.

  15. 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

  16. Nonlinear feature extraction for MMW image classification: a supervised approach

    NASA Astrophysics Data System (ADS)

    Maskall, Guy T.; Webb, Andrew R.

    2002-07-01

    The specular nature of Radar imagery causes problems for ATR as small changes to the configuration of targets can result in significant changes to the resulting target signature. This adds to the challenge of constructing a classifier that is both robust to changes in target configuration and capable of generalizing to previously unseen targets. Here, we describe the application of a nonlinear Radial Basis Function (RBF) transformation to perform feature extraction on millimeter-wave (MMW) imagery of target vehicles. The features extracted were used as inputs to a nearest-neighbor classifier to obtain measures of classification performance. The training of the feature extraction stage was by way of a loss function that quantified the amount of data structure preserved in the transformation to feature space. In this paper we describe a supervised extension to the loss function and explore the value of using the supervised training process over the unsupervised approach and compare with results obtained using a supervised linear technique (Linear Discriminant Analysis --- LDA). The data used were Inverse Synthetic Aperture Radar (ISAR) images of armored vehicles gathered at 94GHz and were categorized as Armored Personnel Carrier, Main Battle Tank or Air Defense Unit. We find that the form of supervision used in this work is an advantage when the number of features used for classification is low, with the conclusion that the supervision allows information useful for discrimination between classes to be distilled into fewer features. When only one example of each class is used for training purposes, the LDA results are comparable to the RBF results. However, when an additional example is added per class, the RBF results are significantly better than those from LDA. Thus, the RBF technique seems better able to make use of the extra knowledge available to the system about variability between different examples of the same class.

  17. Image features of spectral correlation function for arrhythmia classification.

    PubMed

    Khalaf, Aya F; Owis, Mohammed I; Yassine, Inas A

    2015-01-01

    Recently, computerized arrhythmia classification tools have been intensively used to aid physicians to recognize different irregular heartbeats. In this paper, we introduce arrhythmia CAD system exploiting cyclostationary signal analysis through estimation of the spectral correlation function for 5 different beat types. Two experiments were performed. Raw spectral correlation data were used as features in the first experiment while the other experiment which dealt with the spectral correlation coefficients as image included extraction of wavelet and shape features followed by fisher score for dimensionality reduction. As for the classification task, Support Vector Machine (SVM) with linear kernel was used for both experiments. The experimental results showed that both proposed approaches are superior compared to several state of the art methods. This approach achieved sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) of 99.20%, 99.70%, 98.60%, 99.90% and 97.60% respectively.

  18. 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.

  19. Plasmacytoid urothelial carcinoma (PUC): Imaging features with histopathological correlation

    PubMed Central

    Chung, Andrew D.; Schieda, Nicola; Flood, Trevor A.; Cagiannos, Ilias; Mai, Kien T.; Malone, Shawn; Morash, Christopher; Hakim, Shaheed W.; Breau, Rodney H.

    2017-01-01

    Introduction: Plasmacytoid urothelial carcinoma (PUC) is a high-grade variant of conventional urothelial cell carcinoma. This study is the first to describe the imaging findings of PUC, which are previously unreported, using clinical and histopathological correlation. Methods: With internal review board approval, we identified 22 consecutive patients with PUC from 2007–2014. Clinical parameters, including age, gender, therapy, surgical margins, and long-term outcome, were recorded. Baseline imaging was reviewed by an abdominal radiologist who evaluated for tumour detectability/location/morphology, local staging, and presence/location of metastases. Pelvic peritoneal spread of tumour (defined as >5mm thick soft tissue spreading along fascial planes) was also evaluated. Followup imaging was reviewed for presence of local recurrence or metastases. Results: Median age at presentation was 74 years (range 51–86), with only three female patients. Imaging features of the primary tumour in this study were not unique for PUC. Muscle-invasive disease was present on pathology in 19/22 (86%) of tumours, with distant metastases in 2/22 (9%) at baseline imaging. Pelvic peritoneal spread of tumour was radiologically present in 4/20 (20%) at baseline. During followup, recurrent/residual tumour was documented in 16/22 (73%) patients and 7/16 (44%) patients eventually developed distant metastases. Median time to disease recurrence in patients who underwent curative surgery was three months (range 0–19). Conclusions: PUC is an aggressive variant of urothelial carcinoma with poor prognosis. Pelvic peritoneal spread of tumour as thick sheets extending along fascial planes may represent a characteristic imaging finding of locally advanced PUC. PMID:28163816

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

    NASA Astrophysics Data System (ADS)

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

    2012-02-01

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

  1. 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

  2. Development of an expert system as a diagnostic support of cervical cancer in atypical glandular cells, based on fuzzy logics and image interpretation.

    PubMed

    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.

  3. An image feature-based approach to automatically find images for application to clinical decision support.

    PubMed

    Stanley, R Joe; De, Soumya; Demner-Fushman, Dina; Antani, Sameer; Thoma, George R

    2011-07-01

    The illustrations in biomedical publications often provide useful information in aiding clinicians' decisions when full text searching is performed to find evidence in support of a clinical decision. In this research, image analysis and classification techniques are explored to automatically extract information for differentiating specific modalities to characterize illustrations in biomedical publications, which may assist in the evidence finding process. Global, histogram-based, and texture image illustration features were compared to basis function luminance histogram correlation features for modality-based discrimination over a set of 742 manually annotated images by modality (radiological, photo, etc.) selected from the 2004-2005 issues of the British Journal of Oral and Maxillofacial Surgery. Using a mean shifting supervised clustering technique, automatic modality-based discrimination results as high as 95.57% were obtained using the basis function features. These results compared favorably to other feature categories examined. The experimental results show that image-based features, particularly correlation-based features, can provide useful modality discrimination information.

  4. 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).

  5. 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.

  6. Feature selection applied to ultrasound carotid images segmentation.

    PubMed

    Rosati, Samanta; Molinari, Filippo; Balestra, Gabriella

    2011-01-01

    The automated tracing of the carotid layers on ultrasound images is complicated by noise, different morphology and pathology of the carotid artery. In this study we benchmarked four methods for feature selection on a set of variables extracted from ultrasound carotid images. The main goal was to select those parameters containing the highest amount of information useful to classify the pixels in the carotid regions they belong to. Six different classes of pixels were identified: lumen, lumen-intima interface, intima-media complex, media-adventitia interface, adventitia and adventitia far boundary. The performances of QuickReduct Algorithm (QRA), Entropy-Based Algorithm (EBR), Improved QuickReduct Algorithm (IQRA) and Genetic Algorithm (GA) were compared using Artificial Neural Networks (ANNs). All methods returned subsets with a high dependency degree, even if the average classification accuracy was about 50%. Among all classes, the best results were obtained for the lumen. Overall, the four methods for feature selection assessed in this study return comparable results. Despite the need for accuracy improvement, this study could be useful to build a pre-classifier stage for the optimization of segmentation performance in ultrasound automated carotid segmentation.

  7. Motor features in posterior cortical atrophy and their imaging correlates☆

    PubMed Central

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

    2014-01-01

    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

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

    PubMed Central

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

    2013-01-01

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

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

    SciTech Connect

    Wang, X; Chang, J

    2014-06-01

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

  10. Tuberculosis of the genitourinary tract: imaging features with pathological correlation.

    PubMed

    Muttarak, M; ChiangMai, W N; Lojanapiwat, B

    2005-10-01

    The prevalence of pulmonary and extrapulmonary tuberculosis (TB) has been increasing over the past decade, due to the rising number of people with acquired immunodeficiency syndrome and the development of drug-resistant strains of Mycobacterium tuberculosis. The genitourinary tract is the most common site of extrapulmonary TB. Diagnosis is often difficult because TB has a variety of clinical and radiological findings. It can mimic numerous other disease entities. A high level of clinical suspicion and familiarity with various radiological manifestations of TB allow early diagnosis and timely initiation of proper management. This pictorial essay illustrates the spectrum of imaging features of TB affecting the kidney, ureter, bladder, and the female and male genital tracts.

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

    SciTech Connect

    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 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.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.

  12. 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

  13. 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.

  14. Spontaneous heparin-induced thrombocytopenia syndrome without any proximate heparin exposure, infection, or inflammatory condition: Atypical clinical features with heparin-dependent platelet activating antibodies.

    PubMed

    Okata, Takuya; Miyata, Shigeki; Miyashita, Fumio; Maeda, Takuma; Toyoda, Kazunori

    2015-01-01

    Recent studies suggest that a thromboembolic disorder resembling heparin-induced thrombocytopenia (HIT), so-called spontaneous HIT syndrome, can occur in patients without any history of heparin exposure. It is likely due to anti-platelet factor 4 (PF4)/polyanion antibodies induced by other polyanions, such as bacterial surfaces and nucleic acids. We describe an atypical case of spontaneous HIT syndrome. A 70-year-old man suddenly presented with acute cerebral sinus thrombosis (CST). Soon after the initiation of unfractionated heparin (UFH) for the treatment of CST, his platelet count fell precipitously and he developed deep vein thrombosis, a clinical picture consistent with rapid-onset HIT but without any proximate episodes of heparin exposure, infection, trauma, surgery, or other acute illness. Antigen assays and a washed platelet activation assay indicated that the patient already possessed anti-PF4/heparin IgG antibodies with heparin-dependent platelet activation properties on admission. Cessation of UFH and initiation of argatroban resulted in prompt recovery of his platelet count without further thromboembolic events. We identified two similar cases in the literature. However, these patients do not meet the recently proposed criteria for spontaneous HIT syndrome. Even in atypical cases, however, inappropriate or delayed diagnosis of HIT appears to be associated with worse outcomes. We propose that these atypical cases should be included in the category of spontaneous HIT syndrome.

  15. 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.

  16. Histological Image Feature Mining Reveals Emergent Diagnostic Properties for Renal Cancer.

    PubMed

    Kothari, Sonal; Phan, John H; Young, Andrew N; Wang, May D

    2011-11-01

    Computer-aided histological image classification systems are important for making objective and timely cancer diagnostic decisions. These systems use combinations of image features that quantify a variety of image properties. Because researchers tend to validate their diagnostic systems on specific cancer endpoints, it is difficult to predict which image features will perform well given a new cancer endpoint. In this paper, we define a comprehensive set of common image features (consisting of 12 distinct feature subsets) that quantify a variety of image properties. We use a data-mining approach to determine which feature subsets and image properties emerge as part of an "optimal" diagnostic model when applied to specific cancer endpoints. Our goal is to assess the performance of such comprehensive image feature sets for application to a wide variety of diagnostic problems. We perform this study on 12 endpoints including 6 renal tumor subtype endpoints and 6 renal cancer grade endpoints. Keywords-histology, image mining, computer-aided diagnosis.

  17. 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.

  18. Semantic Feature Extraction for Brain CT Image Clustering Using Nonnegative Matrix Factorization

    NASA Astrophysics Data System (ADS)

    Liu, Weixiang; Peng, Fei; Feng, Shu; You, Jiangsheng; Chen, Ziqiang; Wu, Jian; Yuan, Kehong; Ye, Datian

    Brain computed tomography (CT) image based computer-aided diagnosis (CAD) system is helpful for clinical diagnosis and treatment. However it is challenging to extract significant features for analysis because CT images come from different people and CT operator. In this study, we apply nonnegative matrix factorization to extract both appearance and histogram based semantic features of images for clustering analysis as test. Our experimental results on normal and tumor CT images demonstrate that NMF can discover local features for both visual content and histogram based semantics, and the clustering results show that the semantic image features are superior to low level visual features.

  19. 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

  20. Atypical leiomyoma: An unusual variant of cutaneous pilar leiomyoma.

    PubMed

    Nocito, Mabel Jimena; Lustia, María Marcela; Luna, Paula Carolina; Cañadas, Nadia Guadalupe; Castellanos Posse, María Laura; Marchesi, Carolina; Carabajal, Graciela; Mazzini, Miguel Angel

    2009-03-15

    Cutaneous atypical leiomyoma is an unusual benign tumor arising from arrector pili muscle that shares histological features with uterine atypical or symplastic leiomyoma: atypical cellularity with pleomorphic nuclei but minimal or no mitosis. Six other cases have been reported so far and, in spite of its name and of being a smooth muscle proliferation, no recurrences nor metastasis have been reported.

  1. 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.

  2. 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.

  3. Dual Tracer PET Imaging (68Ga-DOTATATE and 18F-FDG) Features in Pulmonary Carcinoid: Correlation with Tumor Proliferation Index

    PubMed Central

    Bhatkar, Dhiraj; Utpat, Ketaki; Basu, Sandip; Joshi, Jyotsna M.

    2017-01-01

    Pulmonary carcinoid tumors are rare group of lung neoplasms representing 1% of all the lung tumors. The typical bronchial carcinoids showed higher and more selective uptake of 68Ga-DOTATATE than of 18F-FDG on PET-CT. The Ki-67(MIB-1), a tumor proliferation index is a prognostic marker in neuroendocrine tumors for estimating tumor progression. Atypical carcinoids have higher Ki-67 index and have an increased propensity to metastasize as compared to typical ones. 68Ga-DOTATATE PET imaging along with Ki-67 can be correlated for better management of patients with neuroendocrine tumors. We describe the dual tracer imaging features in a patient of pulmonary carcinoid with avid 68Ga-DOTATATE and minimal 18FDG (18Flurodeoxyglucose) uptake diagnosed on the basis of imaging and bronchoscopic biopsy and its correlation with tumor proliferation index. PMID:28242984

  4. 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.

  5. 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

  6. 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.

  7. 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.

  8. Evaluation of deformable image registration and a motion model in CT images with limited features

    NASA Astrophysics Data System (ADS)

    Liu, F.; Hu, Y.; Zhang, Q.; Kincaid, R.; Goodman, K. A.; Mageras, G. S.

    2012-05-01

    Deformable image registration (DIR) is increasingly used in radiotherapy applications and provides the basis for a previously described model of patient-specific respiratory motion. We examine the accuracy of a DIR algorithm and a motion model with respiration-correlated CT (RCCT) images of software phantom with known displacement fields, physical deformable abdominal phantom with implanted fiducials in the liver and small liver structures in patient images. The motion model is derived from a principal component analysis that relates volumetric deformations with the motion of the diaphragm or fiducials in the RCCT. Patient data analysis compares DIR with rigid registration as ground truth: the mean ± standard deviation 3D discrepancy of liver structure centroid positions is 2.0 ± 2.2 mm. DIR discrepancy in the software phantom is 3.8 ± 2.0 mm in lung and 3.7 ± 1.8 mm in abdomen; discrepancies near the chest wall are larger than indicated by image feature matching. Marker's 3D discrepancy in the physical phantom is 3.6 ± 2.8 mm. The results indicate that visible features in the images are important for guiding the DIR algorithm. Motion model accuracy is comparable to DIR, indicating that two principal components are sufficient to describe DIR-derived deformation in these datasets.

  9. 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.…

  10. Unsupervised feature selection in digital mammogram image using rough set theory.

    PubMed

    Thangavel, K; Velayutham, C

    2012-01-01

    Feature Selection (FS) is a process which attempts to select features which are more informative. In this paper, a novel unsupervised FS in mammogram images, using rough set-based relative dependency measures, is proposed. A typical mammogram image processing system generally consists of mammogram image acquisition, pre-processing of image, segmentation and features extraction from the segmented mammogram image. The proposed unsupervised FS method is used to select features from data sets; the method is compared with existing rough set based supervised FS methods, and the classification performance of both methods are recorded and demonstrate the efficiency of this method.

  11. 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.

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

    DOE PAGES

    Kashiv, Yoav; Austin, Jotham R.; Lai, Barry; ...

    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

  13. 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.

  14. Sonographic detection and follow up of an atypical pineal cyst: a comparison with magnetic resonance imaging. Case report.

    PubMed

    Harrer, Judith U; Klötzsch, Christof; Oertel, Markus F; Möller-Hartmann, Walter

    2005-09-01

    The incidental ultrasonographic detection of an asymptomatic cystic pineal lesion in a young woman is described and compared with findings on magnetic resonance (MR) images. Follow-up studies obtained using both imaging modalities are presented. The results indicate that transcranial ultrasonography may represent an easy and cost-effective imaging technique for follow up of cystic lesions of the pineal gland, especially in patients unable to undergo MR imaging.

  15. Efficient Content-based Image Retrieval using Support Vector Machines for Feature Aggregation

    NASA Astrophysics Data System (ADS)

    Dimitrovski, Ivica; Loskovska, Suzana; Chorbev, Ivan

    In this paper, a content-based image retrieval system for aggregation and combination of different image features is presented. Feature aggregation is important technique in general content-based image retrieval systems that employ multiple visual features to characterize image content. We introduced and evaluated linear combination and support vector machines to fuse the different image features. The implemented system has several advantages over the existing content-based image retrieval systems. Several implemented features included in our system allow the user to adapt the system to the query image. The SVM-based approach for ranking retrieval results helps processing specific queries for which users do not have knowledge about any suitable descriptors.

  16. 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

  17. Importance of the texture features in a query from a spectral image database

    NASA Astrophysics Data System (ADS)

    Kohonen, Oili; Hauta-Kasari, Markku

    2006-01-01

    A new, semantically meaningful technique for querying the images from a spectral image database is proposed. The technique is based on the use of both color- and texture features. The color features are calculated from spectral images by using the Self-Organizing Map (SOM) when methods of Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) are used for constructing the texture features. The importance of texture features in a querying is seen in experimental results, which are given by using a real spectral image database. Also the differences between the results gained by the use of co-occurrence matrix and LBP are introduced.

  18. Emotional textile image classification based on cross-domain convolutional sparse autoencoders with feature selection

    NASA Astrophysics Data System (ADS)

    Li, Zuhe; Fan, Yangyu; Liu, Weihua; Yu, Zeqi; Wang, Fengqin

    2017-01-01

    We aim to apply sparse autoencoder-based unsupervised feature learning to emotional semantic analysis for textile images. To tackle the problem of limited training data, we present a cross-domain feature learning scheme for emotional textile image classification using convolutional autoencoders. We further propose a correlation-analysis-based feature selection method for the weights learned by sparse autoencoders to reduce the number of features extracted from large size images. First, we randomly collect image patches on an unlabeled image dataset in the source domain and learn local features with a sparse autoencoder. We then conduct feature selection according to the correlation between different weight vectors corresponding to the autoencoder's hidden units. We finally adopt a convolutional neural network including a pooling layer to obtain global feature activations of textile images in the target domain and send these global feature vectors into logistic regression models for emotional image classification. The cross-domain unsupervised feature learning method achieves 65% to 78% average accuracy in the cross-validation experiments corresponding to eight emotional categories and performs better than conventional methods. Feature selection can reduce the computational cost of global feature extraction by about 50% while improving classification performance.

  19. 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

  20. Gaussian MRF rotation-invariant features for image classification.

    PubMed

    Deng, Huawu; Clausi, David A

    2004-07-01

    Features based on Markov random field (MRF) models are sensitive to texture rotation. This paper develops an anisotropic circular Gaussian MRF (ACGMRF) model for retrieving rotation-invariant texture features. To overcome the singularity problem of the least squares estimate method, an approximate least squares estimate method is designed and implemented. Rotation-invariant features are obtained from the ACGMRF model parameters using the discrete Fourier transform. The ACGMRF model is demonstrated to be a statistical improvement over three published methods. The three methods include a Laplacian pyramid, an isotropic circular GMRF (ICGMRF), and gray level cooccurrence probability features.

  1. Forensics Image Background Matching Using Scale Invariant Feature Transform (SIFT) And Speeded Up Robust Features (SURF)

    DTIC Science & Technology

    2007-12-20

    shoe was that? The use of computerised image database to assist in identification”. Forensic Science International , 82(1):7–20, 9/15 1996. 3. Bay...biometric systems”. Forensic science international , 155(2-3):126–140, 2005. 7. Haibin Ling; Jacobs, D.W. “Deformation invariant image matching”. Computer...Image match- ing algorithms for breech face marks and firing pins in a database of spent car- tridge cases of firearms”. Forensic science international , 2001

  2. Discriminative feature representation: an effective postprocessing solution to low dose CT imaging

    NASA Astrophysics Data System (ADS)

    Chen, Yang; Liu, Jin; Hu, Yining; Yang, Jian; Shi, Luyao; Shu, Huazhong; Gui, Zhiguo; Coatrieux, Gouenou; Luo, Limin

    2017-03-01

    This paper proposes a concise and effective approach termed discriminative feature representation (DFR) for low dose computerized tomography (LDCT) image processing, which is currently a challenging problem in medical imaging field. This DFR method assumes LDCT images as the superposition of desirable high dose CT (HDCT) 3D features and undesirable noise-artifact 3D features (the combined term of noise and artifact features induced by low dose scan protocols), and the decomposed HDCT features are used to provide the processed LDCT images with higher quality. The target HDCT features are solved via the DFR algorithm using a featured dictionary composed by atoms representing HDCT features and noise-artifact features. In this study, the featured dictionary is efficiently built using physical phantom images collected from the same CT scanner as the target clinical LDCT images to process. The proposed DFR method also has good robustness in parameter setting for different CT scanner types. This DFR method can be directly applied to process DICOM formatted LDCT images, and has good applicability to current CT systems. Comparative experiments with abdomen LDCT data validate the good performance of the proposed approach. This research was supported by National Natural Science Foundation under grants (81370040, 81530060), the Fundamental Research Funds for the Central Universities, and the Qing Lan Project in Jiangsu Province.

  3. Image Feature Detection and Matching in Underwater Conditions

    DTIC Science & Technology

    2010-04-01

    Natural Waters ], Academic Press (1994). Jaffe, J., " Monte carlo modeling of underwater-image formation: Validity of the linear and small-angle...blurring, whereas corners, which are finer details, would be obscured in more turbid water . In terms of the raw number of correspondences on the bear image...image analysis is to overcome the effects oi’ blurring due to the strong scattering of light by the water and its constituents. This blurring adds

  4. 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.

  5. 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.

  6. Mild Clinical Features and Histopathologically Atypical Cores in Two Korean Families with Central Core Disease Harboring RYR1 Mutations at the C-Terminal Region

    PubMed Central

    Jung, Na-Yeon; Park, Yeong-Eun; Shin, Jin-Hong; Lee, Chang Hun; Jung, Dae-Soo

    2015-01-01

    Background Central core disease (CCD) is a congenital myopathy characterized by distinctive cores in muscle fibers. Mutations in the gene encoding ryanodine receptor 1 (RYR1) have been identified in most CCD patients. Case Report Two unrelated patients presented with slowly progressive or nonprogressive proximal muscle weakness since childhood. Their family history revealed some members with the same clinical problem. Histological analysis of muscle biopsy samples revealed numerous peripheral cores in the muscle fibers. RYR1 sequence analysis disclosed a novel mutation in exon 101 (c.14590T>C) and confirmed a previously reported mutation in exon 102 (c.14678G>A). Conclusions We report herein two families with CCD in whom missense mutations at the C-terminal of RYR1 were identified. Although it has been accepted that such mutations are usually associated with a severe clinical phenotype and clearly demarcated central cores, our patients exhibited a mild clinical phenotype without facial muscle involvement and skeletal deformities, and atypical cores in their muscle biopsy specimens. PMID:25628744

  7. Codebook Guided Feature-Preserving for Recognition-Oriented Image Retargeting.

    PubMed

    Yan, Bo; Tan, Weimin; Li, Ke; Tian, Qi

    2017-03-13

    Traditional image resizing methods, such as uniform scaling and content-aware image retargeting, are designed to preserve the visually salient contents of an image while resizing it. In this paper, we propose a novel image resizing approach called recognition-oriented image retargeting. Its goal is to preserve the distinctive local features for recognition instead of the traditional visual saliency during resizing. Moreover, we also apply our approach to image matching and image retrieval applications to verify its performance. Meanwhile, using our approach to these applications is able to solve some of the challenging problems in their fields. In image matching application, we find that our approach shows promising preservation of local feature descriptors. In image retrieval task, extensive experiments on Oxford5K, Holidays, Paris, and Flickr100k datasets demonstrate that our approach consistently outperforms other image retargeting methods by large margins in the aspects of retrieval precision and query bits.

  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.

  9. Wavelet Algorithm for Feature Identification and Image Analysis

    SciTech Connect

    Moss, William C.; Haase, Sebastian; Sedat, John W.

    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)

  10. 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.

  11. 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.

  12. Physical Features of Visual Images Affect Macaque Monkey’s Preference for These Images

    PubMed Central

    Funahashi, Shintaro

    2016-01-01

    Animals exhibit different degrees of preference toward various visual stimuli. In addition, it has been shown that strongly preferred stimuli can often act as a reward. The aim of the present study was to determine what features determine the strength of the preference for visual stimuli in order to examine neural mechanisms of preference judgment. We used 50 color photographs obtained from the Flickr Material Database (FMD) as original stimuli. Four macaque monkeys performed a simple choice task, in which two stimuli selected randomly from among the 50 stimuli were simultaneously presented on a monitor and monkeys were required to choose either stimulus by eye movements. We considered that the monkeys preferred the chosen stimulus if it continued to look at the stimulus for an additional 6 s and calculated a choice ratio for each stimulus. Each monkey exhibited a different choice ratio for each of the original 50 stimuli. They tended to select clear, colorful and in-focus stimuli. Complexity and clarity were stronger determinants of preference than colorfulness. Images that included greater amounts of spatial frequency components were selected more frequently. These results indicate that particular physical features of the stimulus can affect the strength of a monkey’s preference and that the complexity, clarity and colorfulness of the stimulus are important determinants of this preference. Neurophysiological studies would be needed to examine whether these features of visual stimuli produce more activation in neurons that participate in this preference judgment. PMID:27853424

  13. Physical Features of Visual Images Affect Macaque Monkey's Preference for These Images.

    PubMed

    Funahashi, Shintaro

    2016-01-01

    Animals exhibit different degrees of preference toward various visual stimuli. In addition, it has been shown that strongly preferred stimuli can often act as a reward. The aim of the present study was to determine what features determine the strength of the preference for visual stimuli in order to examine neural mechanisms of preference judgment. We used 50 color photographs obtained from the Flickr Material Database (FMD) as original stimuli. Four macaque monkeys performed a simple choice task, in which two stimuli selected randomly from among the 50 stimuli were simultaneously presented on a monitor and monkeys were required to choose either stimulus by eye movements. We considered that the monkeys preferred the chosen stimulus if it continued to look at the stimulus for an additional 6 s and calculated a choice ratio for each stimulus. Each monkey exhibited a different choice ratio for each of the original 50 stimuli. They tended to select clear, colorful and in-focus stimuli. Complexity and clarity were stronger determinants of preference than colorfulness. Images that included greater amounts of spatial frequency components were selected more frequently. These results indicate that particular physical features of the stimulus can affect the strength of a monkey's preference and that the complexity, clarity and colorfulness of the stimulus are important determinants of this preference. Neurophysiological studies would be needed to examine whether these features of visual stimuli produce more activation in neurons that participate in this preference judgment.

  14. Change detection in high resolution SAR images based on multiscale texture features

    NASA Astrophysics Data System (ADS)

    Wen, Caihuan; Gao, Ziqiang

    2011-12-01

    This paper studied on change detection algorithm of high resolution (HR) Synthetic Aperture Radar (SAR) images based on multi-scale texture features. Firstly, preprocessed multi-temporal Terra-SAR images were decomposed by 2-D dual tree complex wavelet transform (DT-CWT), and multi-scale texture features were extracted from those images. Then, log-ratio operation was utilized to get difference images, and the Bayes minimum error theory was used to extract change information from difference images. Lastly, precision assessment was done. Meanwhile, we compared with the result of method based on texture features extracted from gray-level cooccurrence matrix (GLCM). We had a conclusion that, change detection algorithm based on multi-scale texture features has a great more improvement, which proves an effective method to change detect of high spatial resolution SAR images.

  15. Implementation of High Dimensional Feature Map for Segmentation of MR Images

    PubMed Central

    He, Renjie; Sajja, Balasrinivasa Rao; Narayana, Ponnada A.

    2005-01-01

    A method that considerably reduces the computational and memory complexities associated with the generation of high dimensional (≥3) feature maps for image segmentation is described. The method is based on the K-nearest neighbor (KNN) classification and consists of two parts: preprocessing of feature space and fast KNN. This technique is implemented on a PC and applied for generating three-and four-dimensional feature maps for segmenting MR brain images of multiple sclerosis patients. PMID:16240091

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

    ERIC Educational Resources Information Center

    Lee, Moon-Chuen; Pun, Chi-Man

    2003-01-01

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

  17. 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.

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

    NASA Astrophysics Data System (ADS)

    Li, Jian

    1994-09-01

    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.

  20. 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

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

    PubMed

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

    2014-06-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2010-03-01

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

  3. Histological Image Feature Mining Reveals Emergent Diagnostic Properties for Renal Cancer

    PubMed Central

    Kothari, Sonal; Phan, John H.; Young, Andrew N.; Wang, May D.

    2016-01-01

    Computer-aided histological image classification systems are important for making objective and timely cancer diagnostic decisions. These systems use combinations of image features that quantify a variety of image properties. Because researchers tend to validate their diagnostic systems on specific cancer endpoints, it is difficult to predict which image features will perform well given a new cancer endpoint. In this paper, we define a comprehensive set of common image features (consisting of 12 distinct feature subsets) that quantify a variety of image properties. We use a data-mining approach to determine which feature subsets and image properties emerge as part of an “optimal” diagnostic model when applied to specific cancer endpoints. Our goal is to assess the performance of such comprehensive image feature sets for application to a wide variety of diagnostic problems. We perform this study on 12 endpoints including 6 renal tumor subtype endpoints and 6 renal cancer grade endpoints. Keywords-histology, image mining, computer-aided diagnosis PMID:28163980

  4. 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.

  5. 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

  6. Automatic Image Registration of Multi-Modal Remotely Sensed Data with Global Shearlet Features

    NASA Technical Reports Server (NTRS)

    Murphy, James M.; Le Moigne, Jacqueline; Harding, David J.

    2016-01-01

    Automatic image registration is the process of aligning two or more images of approximately the same scene with minimal human assistance. Wavelet-based automatic registration methods are standard, but sometimes are not robust to the choice of initial conditions. That is, if the images to be registered are too far apart relative to the initial guess of the algorithm, the registration algorithm does not converge or has poor accuracy, and is thus not robust. These problems occur because wavelet techniques primarily identify isotropic textural features and are less effective at identifying linear and curvilinear edge features. We integrate the recently developed mathematical construction of shearlets, which is more effective at identifying sparse anisotropic edges, with an existing automatic wavelet-based registration algorithm. Our shearlet features algorithm produces more distinct features than wavelet features algorithms; the separation of edges from textures is even stronger than with wavelets. Our algorithm computes shearlet and wavelet features for the images to be registered, then performs least squares minimization on these features to compute a registration transformation. Our algorithm is two-staged and multiresolution in nature. First, a cascade of shearlet features is used to provide a robust, though approximate, registration. This is then refined by registering with a cascade of wavelet features. Experiments across a variety of image classes show an improved robustness to initial conditions, when compared to wavelet features alone.

  7. Similar Reference Image Quality Assessment: A New Database and A Trial with Local Feature Matching

    NASA Astrophysics Data System (ADS)

    Lu, Qingbo; Zhou, Wengang; Li, Houqiang

    2016-12-01

    Conventionally, the reference image for image quality assessment (IQA) is completely available (full-reference IQA) or unavailable (no-reference IQA). Even for reduced-reference IQA, the features that are used to predict image quality are still extracted from the pristine reference image. However, the pristine reference image is always unavailable in many real scenarios. In contrast, it is convenient to obtain a number of similar reference images via retrieval from the Internet. These similar reference images may share similar contents and scenes with the image to be assessed. In this paper, we attempt to discuss the image quality assessment problem from the view of similar images, i.e. similar reference IQA. Although the similar reference images share similar contents with the degraded image, the difference between them still cannot be ignored. Therefore, we propose an IQA framework based on local feature matching, which can help to identify the similar regions and structures. Then the IQA features are computed only from these similar regions to predict the final image quality score. Besides, since there is no IQA databases for the similar reference IQA problem, we establish a novel IQA database that consists of 272 images from four scenes. The experiments demonstrate that the performance of our scheme goes beyond state-of-the-art no-reference IQA methods and some full-reference IQA algorithms.

  8. 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

  9. Precoccygeal epidermal inclusion cyst: ultrasound and MR imaging features.

    PubMed

    Halefoglu, A M; Sen, E Y

    2012-01-01

    In this case report, we are presenting a 33 year-old pregnant woman who suffered from pelvic and coccygeal pain. Her medical examination and laboratory tests were found within normal limits. In order to explain her pain, initially a pelvic ultrasound was performed which revealed a huge hypoechoic cystic mass in the precoccygeal-presacral region. She then underwent a pelvic magnetic resonance imaging (MRI) examination in order to better delineate the characteristics and extension of this huge mass. On these images the mass was hypointense on T1 weighted images and extremely hyperintense on T2 weighted images. We also performed a diffusion weighted sequence which exhibited high signal intensity for the mass. We thought that this finding could be suggestive of an epidermal inclusion cyst similar to that of a brain epidermoid cyst which shows bright signal intensity on diffusion weighted images. The patient was operated and the cystic mass removed from the precoccygeal region. Histopathological examination confirmed the diagnosis of epidermal inclusion cyst. This case report suggests that an epidermal inclusion cyst should be considered in the differential diagnosis of intractable pelvic and coccygeal pain. MRI can help to establish the correct diagnosis.

  10. 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.

  11. 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.

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

    PubMed Central

    Paley, Miranda A.

    2016-01-01

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

  13. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2012-03-01

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

  15. 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…

  16. 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

  17. 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.

  18. TIN based image segmentation for man-made feature extraction

    NASA Astrophysics Data System (ADS)

    Jiang, Wanshou; Xie, Junfeng

    2005-10-01

    Traditionally, the splitting and merging algorithm of image segmentation is based on quad tree data structure, which is not convenient to express the topography of regions, the line segments and other information. A new framework is discussed in this paper. It is "TIN based image segmentation and grouping", in which edge information and region information are integrated directly. Firstly, the constrained triangle mesh is constructed with edge segments extracted by EDISON or other algorithm. And then, region growing based on triangles is processed to generate a coarse segmentation. At last, the regions are combined further with perceptual organization rule.

  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. X-ray image enhancement via determinant based feature selection.

    PubMed

    Tappenden, R; Hegarty, J; Broughton, R; Butler, A; Coope, I; Renaud, P

    2013-12-01

    Previous work has investigated the feasibility of using Eigenimage-based enhancement tools to highlight abnormalities on chest X-rays (Butler et al in J Med Imaging Radiat Oncol 52:244-253, 2008). While promising, this approach has been limited by computational restrictions of standard clinical workstations, and uncertainty regarding what constitutes an adequate sample size. This paper suggests an alternative mathematical model to the above referenced singular value decomposition method, which can significantly reduce both the required sample size and the time needed to perform analysis. Using this approach images can be efficiently separated into normal and abnormal parts, with the potential for rapid highlighting of pathology.

  1. 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

  2. Atypical cell populations associated with acquired resistance to cytostatics and cancer stem cell features: the role of mitochondria in nuclear encapsulation.

    PubMed

    Díaz-Carballo, David; 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

    2014-11-01

    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

  3. 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

  4. 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

  5. An improved retinal vessel segmentation method based on high level features for pathological images.

    PubMed

    Ganjee, Razieh; Azmi, Reza; Gholizadeh, Behrouz

    2014-09-01

    Most of the retinal blood vessel segmentation approaches use low level features, resulting in segmenting non-vessel structures together with vessel structures in pathological retinal images. In this paper, a new segmentation method based on high level features is proposed which can process the structure of vessel and non-vessel independently. In this method, segmentation is done in two steps. First, using low level features segmentation is accomplished. Second, using high level features, the non-vessel components are removed. For evaluation, STARE database is used which is publicly available in this field. The results show that the proposed method has 0.9536 accuracy and 0.0191 false positive average on all images of the database and 0.9542 accuracy and 0.0236 false positive average on pathological images. Therefore, the proposed approach shows acceptable accuracy on all images compared to other state of the art methods, and the least false positive average on pathological images.

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

    NASA Astrophysics Data System (ADS)

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

    2006-05-01

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

  7. 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.

  8. Cloud Detection Method Based on Feature Extraction in Remote Sensing Images

    NASA Astrophysics Data System (ADS)

    Changhui, Y.; Yuan, Y.; Minjing, M.; Menglu, Z.

    2013-05-01

    In remote sensing images, the existence of the clouds has a great impact on the image quality and subsequent image processing, as the images covered with clouds contain little useful information. Therefore, the detection and recognition of clouds is one of the major problems in the application of remote sensing images. Present there are two categories of method to cloud detection. One is setting spectrum thresholds based on the characteristics of the clouds to distinguish them. However, the instability and uncertainty of the practical clouds makes this kind of method complexity and weak adaptability. The other method adopts the features in the images to identify the clouds. Since there will be significant overlaps in some features of the clouds and grounds, the detection result is highly dependent on the effectiveness of the features. This paper presented a cloud detection method based on feature extraction for remote sensing images. At first, find out effective features through training pattern, the features are selected from gray, frequency and texture domains. The different features in the three domains of the training samples are calculated. Through the result of statistical analysis of all the features, the useful features are picked up to form a feature set. In concrete, the set includes three feature vectors, respectively, the gray feature vector constituted of average gray, variance, first-order difference, entropy and histogram, the frequency feature vector constituted of DCT high frequency coefficient and wavelet high frequency coefficient, and the texture feature vector constituted of the hybrid entropy and difference of the gray-gradient co-occurrence matrix and the image fractal dimension. Secondly, a thumbnail will be obtained by down sampling the original image and its features of gray, frequency and texture are computed. Last but not least, the cloud region will be judged by the comparison between the actual feature values and the thresholds

  9. 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

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

    PubMed

    Zamir, Amir Roshan; Shah, Mubarak

    2014-08-01

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

  11. Cartographic feature extraction with integrated SIR-B and Landsat TM images

    NASA Technical Reports Server (NTRS)

    Welch, R.; Ehlers, Manfred

    1988-01-01

    A digital cartographic multisensor image database of excellent geometry and improved resolution was created by registering SIR-B images to a rectified Landsat TM reference image and applying intensity-hue-saturation enhancement techniques. When evaluated against geodetic control, RMSE(XY) values of approximately + or - 20 m were noted for the composite SIR-B/TM images. The completeness of cartographic features extracted from the composite images exceeded those obtained from separate SIR-B and TM image data sets by approximately 10 and 25 percent, respectively, indicating that the composite images may prove suitable for planimetric mapping at a scale of 1:100,000 or smaller. At present, the most effective method for extracting cartographic information involves digitizing features directly from the image processing display screen.

  12. Adaptive descriptor based on the geometric consistency of local image features: application to flower image classification

    NASA Astrophysics Data System (ADS)

    Najjar, Asma; Zagrouba, Ezzeddine

    2016-09-01

    Geometric consistency is, usually, considered as a postprocessing step to filter matched sets of local features in order to discard outliers. In this work, it is used to propose an adaptive feature that describes the geometric dispersion of keypoints. It is based on a distribution computed by a nonparametric estimator so that no assumption is made about the data. We investigated and discussed the invariance properties of our descriptor under the most common two- and three-dimensional transformations. Then, we applied it to flower recognition. The classification is performed using the precomputed kernel of support vector machines classifier. Indeed, a similarity computing framework that uses the Kullback-Leibler divergence is presented. Furthermore, a customized layout for each flower image is designed to describe and compare separately the boundary and the central area of flowers. Experimentations made on the Oxford flower-17 dataset prove the efficiency of our method in terms of classification accuracy and computational complexity. The limits of our descriptor are also discussed on a 10-class subset of the Oxford flower-102 dataset.

  13. Spectrum and Image Texture Features Analysis for Early Blight Disease Detection on Eggplant Leaves.

    PubMed

    Xie, Chuanqi; He, Yong

    2016-05-11

    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.

  14. Clinical, biological, and imaging features of monogenic Alzheimer's Disease.

    PubMed

    Pilotto, Andrea; Padovani, Alessandro; Borroni, Barbara

    2013-01-01

    The discovery of monogenic forms of Alzheimer's Disease (AD) associated with mutations within PSEN1, PSEN2, and APP genes is giving a big contribution in the understanding of the underpinning mechanisms of this complex disorder. Compared with sporadic form, the phenotype associated with monogenic cases is somewhat broader including behavioural disturbances, epilepsy, myoclonus, and focal presentations. Structural and functional imaging show typical early changes also in presymptomatic monogenic carriers. Amyloid imaging and CSF tau/A β ratio may be useful in the differential diagnosis with other neurodegenerative dementias, especially, in early onset cases. However, to date any specific biomarkers of different monogenic cases have been identified. Thus, in clinical practice, the early identification is often difficult, but the copresence of different elements could help in recognition. This review will focus on the clinical and instrumental markers useful for the very early identification of AD monogenic cases, pivotal in the development, and evaluation of disease-modifying therapy.

  15. 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

  16. Similarity searching for chest CT images based on object features and spatial relation maps.

    PubMed

    Yu, Sung-Nien; Chiang, Chih-Tsung

    2004-01-01

    In this paper, an object-based image retrieval system for chest CT image databases is proposed. Based on the scheme of the content-based image retrieval method, we proposed an image segmentation method which combines the anatomical knowledge of the chest and the well-known watershed segmentation algorithm. The purpose of segmentation is to identify the mediastinum and the two lung lobes in a chest CT image. The ARGs (attributed relational graphs) are chosen to describe the features of segmented objects. Then, image database is constructed by the feature vectors of images. In database searching, two searching modes are provided that are "query by example" and "query by object". Our system uses Euclidean distance to measure the similarity between the image in query and the image in database. The system output the 30 most similar images in the chest CT image database as query results. The experimental results show that the average precision of our system is about 80% which is impressive in a totally automatic medical image retrieval system. Moreover, query concentrated in certain objects features usually show better result than the regular query by example. The possible reasons are discussed.

  17. Perceptual quality prediction on authentically distorted images using a bag of features approach

    PubMed Central

    Ghadiyaram, Deepti; Bovik, Alan C.

    2017-01-01

    Current top-performing blind perceptual image quality prediction models are generally trained on legacy databases of human quality opinion scores on synthetically distorted images. Therefore, they learn image features that effectively predict human visual quality judgments of inauthentic and usually isolated (single) distortions. However, real-world images usually contain complex composite mixtures of multiple distortions. We study the perceptually relevant natural scene statistics of such authentically distorted images in different color spaces and transform domains. We propose a “bag of feature maps” approach that avoids assumptions about the type of distortion(s) contained in an image and instead focuses on capturing consistencies—or departures therefrom—of the statistics of real-world images. Using a large database of authentically distorted images, human opinions of them, and bags of features computed on them, we train a regressor to conduct image quality prediction. We demonstrate the competence of the features toward improving automatic perceptual quality prediction by testing a learned algorithm using them on a benchmark legacy database as well as on a newly introduced distortion-realistic resource called the LIVE In the Wild Image Quality Challenge Database. We extensively evaluate the perceptual quality prediction model and algorithm and show that it is able to achieve good-quality prediction power that is better than other leading models. PMID:28129417

  18. The impact of clutter variance on feature discrimination in imaging polarimetry

    NASA Astrophysics Data System (ADS)

    Duggin, Michael J.; Cabot, Elizabeth R.

    2004-07-01

    The automated, or semi-automated analysis of scene elements in a clutter background is more complex in polarimetric imaging than in conventional imaging. This is largely due to the fact that misregistration of the orthogonal images used to calculate the Stokes parameter images introduces an artificial clutter. Further, there is little reported information on polarimetric image clutter. We present representative findings from an analysis of polarimetric image data, obtained over various backgrounds with various geometries, and examine the manner in which systematic and random variations impact feature discriminations.

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

    NASA Astrophysics Data System (ADS)

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

    2013-03-01

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

  20. 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.

  1. Integrated local binary pattern texture features for classification of breast tissue imaged by optical coherence microscopy.

    PubMed

    Wan, Sunhua; Lee, Hsiang-Chieh; Huang, Xiaolei; Xu, Ting; Xu, Tao; Zeng, Xianxu; Zhang, Zhan; Sheikine, Yuri; Connolly, James L; Fujimoto, James G; Zhou, Chao

    2017-03-08

    This paper proposes a texture analysis technique that can effectively classify different types of human breast tissue imaged by Optical Coherence Microscopy (OCM). OCM is an emerging imaging modality for rapid tissue screening and has the potential to provide high resolution microscopic images that approach those of histology. OCM images, acquired without tissue staining, however, pose unique challenges to image analysis and pattern classification. We examined multiple types of texture features and found Local Binary Pattern (LBP) features to perform better in classifying tissues imaged by OCM. In order to improve classification accuracy, we propose novel variants of LBP features, namely average LBP (ALBP) and block based LBP (BLBP). Compared with the classic LBP feature, ALBP and BLBP features provide an enhanced encoding of the texture structure in a local neighborhood by looking at intensity differences among neighboring pixels and among certain blocks of pixels in the neighborhood. Fourty-six freshly excised human breast tissue samples, including 27 benign (e.g. fibroadenoma, fibrocystic disease and usual ductal hyperplasia) and 19 breast carcinoma (e.g. invasive ductal carcinoma, ductal carcinoma in situ and lobular carcinoma in situ) were imaged with large field OCM with an imaging area of 10 × 10 mm(2) (10, 000 × 10, 000 pixels) for each sample. Corresponding H&E histology was obtained for each sample and used to provide ground truth diagnosis. 4310 small OCM image blocks (500 × 500 pixels) each paired with corresponding H&E histology was extracted from large-field OCM images and labeled with one of the five different classes: adipose tissue (n = 347), fibrous stroma (n = 2,065), breast lobules (n = 199), carcinomas (pooled from all sub-types, n = 1,127), and background (regions outside of the specimens, n = 572). Our experiments show that by integrating a selected set of LBP and the two new variant (ALBP and BLBP) features at multiple scales, the

  2. Comparison of image features calculated in different dimensions for computer-aided diagnosis of lung nodules

    NASA Astrophysics Data System (ADS)

    Xu, Ye; Lee, Michael C.; Boroczky, Lilla; Cann, Aaron D.; Borczuk, Alain C.; Kawut, Steven M.; Powell, Charles A.

    2009-02-01

    Features calculated from different dimensions of images capture quantitative information of the lung nodules through one or multiple image slices. Previously published computer-aided diagnosis (CADx) systems have used either twodimensional (2D) or three-dimensional (3D) features, though there has been little systematic analysis of the relevance of the different dimensions and of the impact of combining different dimensions. The aim of this study is to determine the importance of combining features calculated in different dimensions. We have performed CADx experiments on 125 pulmonary nodules imaged using multi-detector row CT (MDCT). The CADx system computed 192 2D, 2.5D, and 3D image features of the lesions. Leave-one-out experiments were performed using five different combinations of features from different dimensions: 2D, 3D, 2.5D, 2D+3D, and 2D+3D+2.5D. The experiments were performed ten times for each group. Accuracy, sensitivity and specificity were used to evaluate the performance. Wilcoxon signed-rank tests were applied to compare the classification results from these five different combinations of features. Our results showed that 3D image features generate the best result compared with other combinations of features. This suggests one approach to potentially reducing the dimensionality of the CADx data space and the computational complexity of the system while maintaining diagnostic accuracy.

  3. 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.

  4. Entropy based unsupervised Feature Selection in digital mammogram image using rough set theory.

    PubMed

    Velayutham, C; Thangavel, K

    2012-01-01

    Feature Selection (FS) is a process, which attempts to select features, which are more informative. In the supervised FS methods various feature subsets are evaluated using an evaluation function or metric to select only those features, which are related to the decision classes of the data under consideration. However, for many data mining applications, decision class labels are often unknown or incomplete, thus indicating the significance of unsupervised FS. However, in unsupervised learning, decision class labels are not provided. The problem is that not all features are important. Some of the features may be redundant, and others may be irrelevant and noisy. In this paper, a novel unsupervised FS in mammogram image, using rough set-based entropy measures, is proposed. A typical mammogram image processing system generally consists of mammogram image acquisition, pre-processing of image, segmentation, features extracted from the segmented mammogram image. The proposed method is used to select features from data set, the method is compared with the existing rough set-based supervised FS methods and classification performance of both methods are recorded and demonstrates the efficiency of the method.

  5. A feature-based learning framework for accurate prostate localization in CT images.

    PubMed

    Liao, Shu; Shen, Dinggang

    2012-08-01

    Automatic segmentation of prostate in CT images plays an important role in medical image analysis and image guided radiation therapy. It remains as a challenging problem mainly due to three issues: First, the image contrast between the prostate and its surrounding tissues is low in prostate CT images and no obvious boundaries can be observed. Second, the unpredictable prostate motion causes large position variations of the prostate in the treatment images scanned at different treatment days. Third, the uncertainty of the existence of bowel gas in treatment images significantly changes the image appearance even for images taken from the same patient. To address these issues, in this paper we are motivated to propose a feature based learning framework for accurate prostate localization in CT images. The main contributions of the proposed method lie in the following aspects: (1) Anatomical features are extracted from input images and adopted as signatures for each voxel. The most robust and informative features are identified by the feature selection process to help localize the prostate. (2) Regions with salient features but irrelevant to the localization of prostate, such as regions filled with bowel gas are automatically filtered out by the proposed method. (3) An online update mechanism is adopted in this paper to adaptively combine both population information and patient-specific information to localize the prostate. The proposed method is evaluated on a CT prostate dataset of 24 patients to localize the prostate, where each patient has more than 10 longitudinal images scanned at different treatment times. It is also compared with several state-of- the-art prostate localization algorithms in CT images, and the experimental results demonstrate that the proposed method achieves the highest localization accuracy among all the methods under comparison.

  6. 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.

  7. Differential diagnostic features of the radionuclide scrotal image.

    PubMed

    Mishkin, F S

    1977-01-01

    Differential diagnosis of scrotal lesions is aided by correlating radionuclide images with clinical findings. Subacute torsion is associated with peripheral hyperemia and can be mistaken for an inflammatory process; however, in a review of 128 studies, torsion and orchiectomy were the only processes encountered which had a center truly devoid of activity on the tissue phase compared to the normal side. Other lesions such as acute inflammation, abscess, hematoma, and hemorrhagic tumor may superficially appear to lack central activity but invariably contain at least as much activity when compared to the normal side.

  8. Laser interference effect evaluation method based on character of laser-spot and image feature

    NASA Astrophysics Data System (ADS)

    Tang, Jianfeng; Luo, Xiaolin; Wu, Lingxia

    2016-10-01

    Evaluating the laser interference effect to CCD objectively and accurately has great research value. Starting from the change of the image's feature before and after interference, meanwhile, considering the influence of the laser-spot distribution character on the masking degree of the image feature information, a laser interference effect evaluation method based on character of laser-spot and image feature was proposed. It reflected the laser-spot distribution character using the distance between the center of the laser-spot and center of the target. It reflected the change of the global image feature using the changes of image's sparse coefficient matrix, which was obtained by the SSIM-inspired orthogonal matching pursuit (OMP) sparse coding algorithm. What's more, the assessment method reflected the change of the local image feature using the changes of the image's edge sharpness, which could be obtained by the change of the image's gradient magnitude. Taken together, the laser interference effect can be evaluated accurately. In terms of the laser interference experiment results, the proposed method shows good rationality and feasibility under the disturbing condition of different laser powers, and it can also overcome the inaccuracy caused by the change of the laser-spot position, realizing the evaluation of the laser interference effect objectively and accurately.

  9. Radiomics: extracting more information from medical images using advanced feature analysis.

    PubMed

    Lambin, Philippe; Rios-Velazquez, Emmanuel; Leijenaar, Ralph; Carvalho, Sara; van Stiphout, Ruud G P M; Granton, Patrick; Zegers, Catharina M L; Gillies, Robert; Boellard, Ronald; Dekker, André; Aerts, Hugo J W L

    2012-03-01

    Solid cancers are spatially and temporally heterogeneous. This limits the use of invasive biopsy based molecular assays but gives huge potential for medical imaging, which has the ability to capture intra-tumoural heterogeneity in a non-invasive way. During the past decades, medical imaging innovations with new hardware, new imaging agents and standardised protocols, allows the field to move towards quantitative imaging. Therefore, also the development of automated and reproducible analysis methodologies to extract more information from image-based features is a requirement. Radiomics--the high-throughput extraction of large amounts of image features from radiographic images--addresses this problem and is one of the approaches that hold great promises but need further validation in multi-centric settings and in the laboratory.

  10. Featured Image: A Pulsar Is Obscured by a Solar Explosion

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2016-12-01

    This series of images (click for the full view!), taken by the Solar and Heliospheric Observatory satellite (SOHO) in August 2015, reveals a tremendous outburst of plasma and magnetic field from the Sun: a coronal mass ejection (CME). If you look closely, youll note that as the CME expands from the Suns surface, it passes in front of a dot highlighted in yellow. This dot marks the location of a distant background pulsar, PSR B0950+08. In a recent study led by Tim Howard (Southwest Research Institute), a team of scientists studied the change observed in the radio emission of this pulsar as the CME passed by in the foreground. The team used these observations to estimate the CMEs density and magnetic field measurements that can tell us more about the nature of the magnetic field in the Suns corona and the solar wind.You can check out the animation of this CME, also taken with SOHOs LASCO instrument, below (the CME starts around 20 seconds in), or you can find out more from the original paper!http://cdn.iopscience.com/images/0004-637X/831/2/208/Full/apjaa35ecf1_video.mp4CitationT. A. Howard et al 2016 ApJ 831 208. doi:10.3847/0004-637X/831/2/208

  11. Joint Applied Optics and Chinese Optics Letters feature introduction: digital holography and three-dimensional imaging.

    PubMed

    Poon, Ting-Chung

    2011-12-01

    This feature issue serves as a pilot issue promoting the joint issue of Applied Optics and Chinese Optics Letters. It focuses upon topics of current relevance to the community working in the area of digital holography and 3-D imaging.

  12. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

  14. Glioma Grading Using Cell Nuclei Morphologic Features in Digital Pathology Images

    PubMed Central

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

    2016-01-01

    This work proposes a computationally efficient cell nuclei morphologic feature analysis technique to characterize the brain gliomas in tissue slide images. In this work, our contributions are two-fold: 1) obtain an optimized cell nuclei segmentation method based on the pros and cons of the existing techniques in literature, 2) extract representative features by k-mean clustering of nuclei morphologic features to include area, perimeter, eccentricity, and major axis length. This clustering based representative feature extraction avoids shortcomings of extensive tile [1] [2] and nuclear score [3] based methods for brain glioma grading in pathology images. Multilayer perceptron (MLP) is used to classify extracted features into two tumor types: glioblastoma multiforme (GBM) and low grade glioma (LGG). Quantitative scores such as precision, recall, and accuracy are obtained using 66 clinical patients’ images from The Cancer Genome Atlas (TCGA) [4] dataset. On an average ~94% accuracy from 10 fold cross-validation confirms the efficacy of the proposed method. PMID:27942094

  15. Multipolarimetric SAR image change detection based on multiscale feature-level fusion

    NASA Astrophysics Data System (ADS)

    Sun, X.; Zhang, J.; Zhai, L.

    2015-06-01

    Many methodologies of change detection have been discussed in the literature, but most of them are tested on only optical images or traditional synthetic-aperture radar (SAR) images. Few studies have investigated multipolarimetric SAR image change detection. In this study, we presented a type of multipolarimetric SAR image change detection approach based on nonsubsampled contourlet transform and multiscale feature-level fusion techniques. In this approach, Instead of denoising an image in advance, the nonsubsampled contourlet transform multiscale decomposition was used to reduce the effect of speckle noise by processing only the low-frequency sub-band coefficients of the decomposed image, and the multiscale feature-level fusion technique was employed to integrate the rich information obtained from various polarization images. Because SAR image information is dependent on scale, a multiscale multipolarimetric feature-level fusion strategy is introduced into the change detection to improve change detection precision; this feature-level fusion can not only achieve complementation of information with different polarizations and on different scales, but also has better robustness against noise. Compared with PCA methods, the proposed method constructs better differential images, resulting in higher change detection precision.

  16. Feature and contrast enhancement of mammographic image based on multiscale analysis and morphology.

    PubMed

    Wu, Shibin; Yu, Shaode; Yang, Yuhan; 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).

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

    PubMed Central

    Lahmiri, Salim; Boukadoum, Mounir

    2013-01-01

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

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

    PubMed

    Lahmiri, Salim; Boukadoum, Mounir

    2013-01-01

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

  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

    DOE PAGES

    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

  1. 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.

  2. Featured Image: A Search for Stellar Bow Shock Nebulae

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2017-02-01

    These dynamic infrared images (click for the full view!) reveal what are known as bow shock nebulae nebulae that form at the interface between the interstellar medium and the stellar wind from a high-speed star zipping through the galaxy (the arrows show the direction of motion of the star). When the relative speed between the two is supersonic, an arc-shaped bow shock forms ahead of the star, like the six prototypical ones pictured here. A team of scientists led by Henry Kobulnicky (University of Wyoming) has recently searched through survey data from the Spitzer Space Telescope and the Wide Field Infrared Explorer (WISE) to build a catalog of more than 700 such bow-shock nebula candidates, the vast majority of which are new discoveries. To find out more about their sample, check out the paper below!CitationHenry A. Kobulnicky et al 2016 ApJS 227 18. doi:10.3847/0067-0049/227/2/18

  3. Imaging Features of Pediatric Pentastomiasis Infection: a Case Report

    PubMed Central

    Wang, Xi Qun; Lin, Long; Gao, De Chun; Zhang, Hong Xi; Zhang, Yi Ying; Zhou, Yin Bao

    2010-01-01

    We report here a case of pentastomiasis infection in a 3-year-old girl who had high fever, abdominal pain, abdominal tension and anemia. Ultrasound scanning of the abdomen revealed disseminated hyperechoic nodules in the liver and a small amount of ascites. Abdominal MRI showed marked hepatomegaly with disseminated miliary nodules of high signal intensity throughout the hepatic parenchyma on T2-weighted images; retroperitoneal lymphadenopathy and disseminated miliary nodules on the peritoneum were also noted. Chest CT showed scattered small hyperdense nodules on both sides of the lungs. The laparoscopy demonstrated diffuse white nodules on the liver surface and the peritoneum. After the small intestinal wall and peritoneal biopsy, histological examination revealed parenchymal tubercles containing several larvae of pentastomids and a large amount of inflammatory cell infiltration around them. The pathological diagnosis was parasitic granuloma from pentastomiasis infection. PMID:20592934

  4. 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.

  5. Extraction of Lesion-Partitioned Features and Retrieval of Contrast-Enhanced Liver Images

    PubMed Central

    Yu, Mei; Feng, Qianjin; Yang, Wei; Gao, Yang; Chen, Wufan

    2012-01-01

    The most critical step in grayscale medical image retrieval systems is feature extraction. Understanding the interrelatedness between the characteristics of lesion images and corresponding imaging features is crucial for image training, as well as for features extraction. A feature-extraction algorithm is developed based on different imaging properties of lesions and on the discrepancy in density between the lesions and their surrounding normal liver tissues in triple-phase contrast-enhanced computed tomographic (CT) scans. The algorithm includes mainly two processes: (1) distance transformation, which is used to divide the lesion into distinct regions and represents the spatial structure distribution and (2) representation using bag of visual words (BoW) based on regions. The evaluation of this system based on the proposed feature extraction algorithm shows excellent retrieval results for three types of liver lesions visible on triple-phase scans CT images. The results of the proposed feature extraction algorithm show that although single-phase scans achieve the average precision of 81.9%, 80.8%, and 70.2%, dual- and triple-phase scans achieve 86.3% and 88.0%. PMID:22988480

  6. 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

  7. Acousto-Optic Technology for Topographic Feature Extraction and Image Analysis.

    DTIC Science & Technology

    1981-03-01

    This report contains all findings of the acousto - optic technology study for feature extraction conducted by Deft Laboratories Inc. for the U.S. Army...topographic feature extraction and image analysis using acousto - optic (A-O) technology. A conclusion of this study was that A-O devices are potentially

  8. 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.

  9. 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.

  10. Automated classification of patients with coronary artery disease using grayscale features from left ventricle echocardiographic images.

    PubMed

    Acharya, U Rajendra; Sree, S Vinitha; Muthu Rama Krishnan, M; Krishnananda, N; Ranjan, Shetty; Umesh, Pai; Suri, Jasjit S

    2013-12-01

    Coronary Artery Disease (CAD), caused by the buildup of plaque on the inside of the coronary arteries, has a high mortality rate. To efficiently detect this condition from echocardiography images, with lesser inter-observer variability and visual interpretation errors, computer based data mining techniques may be exploited. We have developed and presented one such technique in this paper for the classification of normal and CAD affected cases. A multitude of grayscale features (fractal dimension, entropies based on the higher order spectra, features based on image texture and local binary patterns, and wavelet based features) were extracted from echocardiography images belonging to a huge database of 400 normal cases and 400 CAD patients. Only the features that had good discriminating capability were selected using t-test. Several combinations of the resultant significant features were used to evaluate many supervised classifiers to find the combination that presents a good accuracy. We observed that the Gaussian Mixture Model (GMM) classifier trained with a feature subset made up of nine significant features presented the highest accuracy, sensitivity, specificity, and positive predictive value of 100%. We have also developed a novel, highly discriminative HeartIndex, which is a single number that is calculated from the combination of the features, in order to objectively classify the images from either of the two classes. Such an index allows for an easier implementation of the technique for automated CAD detection in the computers in hospitals and clinics.

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

    PubMed Central

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

    2014-01-01

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

  12. Feature-Motivated Simplified Adaptive PCNN-Based Medical Image Fusion Algorithm in NSST Domain.

    PubMed

    Ganasala, Padma; Kumar, Vinod

    2016-02-01

    Multimodality medical image fusion plays a vital role in diagnosis, treatment planning, and follow-up studies of various diseases. It provides a composite image containing critical information of source images required for better localization and definition of different organs and lesions. In the state-of-the-art image fusion methods based on nonsubsampled shearlet transform (NSST) and pulse-coupled neural network (PCNN), authors have used normalized coefficient value to motivate the PCNN-processing both low-frequency (LF) and high-frequency (HF) sub-bands. This makes the fused image blurred and decreases its contrast. The main objective of this work is to design an image fusion method that gives the fused image with better contrast, more detail information, and suitable for clinical use. We propose a novel image fusion method utilizing feature-motivated adaptive PCNN in NSST domain for fusion of anatomical images. The basic PCNN model is simplified, and adaptive-linking strength is used. Different features are used to motivate the PCNN-processing LF and HF sub-bands. The proposed method is extended for fusion of functional image with an anatomical image in improved nonlinear intensity hue and saturation (INIHS) color model. Extensive fusion experiments have been performed on CT-MRI and SPECT-MRI datasets. Visual and quantitative analysis of experimental results proved that the proposed method provides satisfactory fusion outcome compared to other image fusion methods.

  13. Relating image-based features to mammogram interpretation

    NASA Astrophysics Data System (ADS)

    Mello-Thoms, Claudia; Nodine, Calvin F.; Kundel, Harold L.

    2002-04-01

    Mammography is a widely used technique to screen for breast cancer. However, due to the complexity of the breast tissue and to the low prevalence of cancer in the screening population, between 10-30% of retrospectively visible cancers are not reported. Faulty visual search, that is, not examining the area where the cancer is located, is responsible for a third of these misses, but all other unreported cancers attract some amount of visual attention, as indicated by the duration of visual gaze in the location of the lesion. Thus, perceptual and decision making mechanisms must be understood, in order to aid radiologists to detect cancer at earlier stages. We have been working on modeling these mechanisms by using spatial frequency analysis, in a process that is inspired by the rules and complexity of the eye-brain system. In this paper we analyze the different decision outcomes of experienced mammographers and less experienced radiology residents, undergoing a mammography rotation, when examining a case set of 40 two-view mammogram cases. We also characterize the interplay between local factors, which are related to the area of the image that attracts visual attention, and global factors, which are related to breast sampling, as they affect decision outcome for each group.

  14. Featured Image: The Simulated Collapse of a Core

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2016-11-01

    This stunning snapshot (click for a closer look!) is from a simulation of a core-collapse supernova. Despite having been studied for many decades, the mechanism driving the explosions of core-collapse supernovae is still an area of active research. Extremely complex simulations such as this one represent best efforts to include as many realistic physical processes as is currently computationally feasible. In this study led by Luke Roberts (a NASA Einstein Postdoctoral Fellow at Caltech at the time), a core-collapse supernova is modeled long-term in fully 3D simulations that include the effects of general relativity, radiation hydrodynamics, and even neutrino physics. The authors use these simulations to examine the evolution of a supernova after its core bounce. To read more about the teams findings (and see more awesome images from their simulations), check out the paper below!CitationLuke F. Roberts et al 2016 ApJ 831 98. doi:10.3847/0004-637X/831/1/98

  15. 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

  16. Uncertainty analysis of quantitative imaging features extracted from contrast-enhanced CT in lung tumors

    PubMed Central

    Yang, Jinzhong; Zhang, Lifei; Fave, Xenia J.; Fried, David V.; Stingo, Francesco C.; Ng, Chaan S.; Court, Laurence E.

    2016-01-01

    Purpose To assess the uncertainty of quantitative imaging features extracted from contrast-enhanced computed tomography (CT) scans of lung cancer patients in terms of the dependency on the time after contrast injection and the feature reproducibility between scans. Methods Eight patients underwent contrast-enhanced CT scans of lung tumors on two sessions 2–7 days apart. Each session included 6 CT scans of the same anatomy taken every 15 seconds, starting 50 seconds after contrast injection. Image features based on intensity histogram, co-occurrence matrix, neighborhood gray-tone difference matrix, run-length matrix, and geometric shape were extracted from the tumor for each scan. Spearman’s correlation was used to examine the dependency of features on the time after contrast injection, with values over 0.50 considered time-dependent. Concordance correlation coefficients were calculated to examine the reproducibility of each feature between times of scans after contrast injection and between scanning sessions, with values greater than 0.90 considered reproducible. Results The features were found to have little dependency on the time between the contrast injection and the CT scan. Most features were reproducible between times of scans after contrast injection and between scanning sessions. Some features were more reproducible when they were extracted from a CT scan performed at a longer time after contrast injection. Conclusion The quantitative imaging features tested here are mostly reproducible and show little dependency on the time after contrast injection. PMID:26745258

  17. 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.

  18. Computed Tomography and Magnetic Resonance Imaging Features of the Temporomandibular Joint in Two Normal Camels

    PubMed Central

    Arencibia, Alberto; Blanco, Diego; González, Nelson; Rivero, Miguel A.

    2012-01-01

    Computed tomography (CT) and magnetic resonance (MR) image features of the temporomandibular joint (TMJ) and associated structures in two mature dromedary camels were obtained with a third-generation equipment CT and a superconducting magnet RM at 1.5 Tesla. Images were acquired in sagittal and transverse planes. Medical imaging processing with imaging software was applied to obtain postprocessing CT and MR images. Relevant anatomic structures were identified and labelled. The resulting images provided excellent anatomic detail of the TMJ and associated structures. Annotated CT and MR images from this study are intended as an anatomical reference useful in the interpretation for clinical CT and MR imaging studies of the TMJ of the dromedary camels. PMID:22567308

  19. Profiles of US and CT imaging features with a high probability of appendicitis

    PubMed Central

    Laméris, W.; van Es, H. W.; ten Hove, W.; Bouma, W. H.; van Leeuwen, M. S.; van Keulen, E. M.; van der Hulst, V. P. M.; Henneman, O. D.; Bossuyt, P. M.; Boermeester, M. A.; Stoker, J.

    2010-01-01

    Objectives To identify and evaluate profiles of US and CT features associated with acute appendicitis. Methods Consecutive patients presenting with acute abdominal pain at the emergency department were invited to participate in this study. All patients underwent US and CT. Imaging features known to be associated with appendicitis, and an imaging diagnosis were prospectively recorded by two independent radiologists. A final diagnosis was assigned after 6 months. Associations between appendiceal imaging features and a final diagnosis of appendicitis were evaluated with logistic regression analysis. Results Appendicitis was assigned to 284 of 942 evaluated patients (30%). All evaluated features were associated with appendicitis. Imaging profiles were created after multivariable logistic regression analysis. Of 147 patients with a thickened appendix, local transducer tenderness and peri-appendiceal fat infiltration on US, 139 (95%) had appendicitis. On CT, 119 patients in whom the appendix was completely visualised, thickened, with peri-appendiceal fat infiltration and appendiceal enhancement, 114 had a final diagnosis of appendicitis (96%). When at least two of these essential features were present on US or CT, sensitivity was 92% (95% CI 89–96%) and 96% (95% CI 93–98%), respectively. Conclusion Most patients with appendicitis can be categorised within a few imaging profiles on US and CT. When two of the essential features are present the diagnosis of appendicitis can be made accurately. PMID:20119730

  20. 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.

  1. 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

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

    PubMed

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

    2016-01-01

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

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

    PubMed

    Paglia, Donald E; Walford, Roy L

    2005-01-01

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

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

    PubMed Central

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

    2015-01-01

    Abstract. 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 (p=0.01) when using this feature alone. PMID:26835496

  5. Building the gist of a scene: the role of global image features in recognition.

    PubMed

    Oliva, Aude; Torralba, Antonio

    2006-01-01

    Humans can recognize the gist of a novel image in a single glance, independent of its complexity. How is this remarkable feat accomplished? On the basis of behavioral and computational evidence, this paper describes a formal approach to the representation and the mechanism of scene gist understanding, based on scene-centered, rather than object-centered primitives. We show that the structure of a scene image can be estimated by the mean of global image features, providing a statistical summary of the spatial layout properties (Spatial Envelope representation) of the scene. Global features are based on configurations of spatial scales and are estimated without invoking segmentation or grouping operations. The scene-centered approach is not an alternative to local image analysis but would serve as a feed-forward and parallel pathway of visual processing, able to quickly constrain local feature analysis and enhance object recognition in cluttered natural scenes.

  6. Breast Cancer Classification From Histological Images with Multiple Features and Random Subspace Classifier Ensemble

    NASA Astrophysics Data System (ADS)

    Zhang, Yungang; Zhang, Bailing; Lu, Wenjin

    2011-06-01

    Histological image is important for diagnosis of breast cancer. In this paper, we present a novel automatic breaset cancer classification scheme based on histological images. The image features are extracted using the Curvelet Transform, statistics of Gray Level Co-occurence Matrix (GLCM) and Completed Local Binary Patterns (CLBP), respectively. The three different features are combined together and used for classification. A classifier ensemble approach, called Random Subspace Ensemble (RSE), are used to select and aggregate a set of base neural network classifiers for classification. The proposed multiple features and random subspace ensemble offer the classification rate 95.22% on a publically available breast cancer image dataset, which compares favorably with the previously published result 93.4%.

  7. Analysis of mammogram images based on texture features of curvelet sub-bands

    NASA Astrophysics Data System (ADS)

    Gardezi, Syed Jamal Safdar; Faye, Ibrahima; Eltoukhy, Mohamed Meselhy

    2014-01-01

    Image texture analysis plays an important role in object detection and recognition in image processing. The texture analysis can be used for early detection of breast cancer by classifying the mammogram images into normal and abnormal classes. This study investigates breast cancer detection using texture features obtained from the grey level cooccurrence matrices (GLCM) of curvelet sub-band levels combined with texture feature obtained from the image itself. The GLCM were constructed for each sub-band of three curvelet decomposition levels. The obtained feature vector presented to the classifier to differentiate between normal and abnormal tissues. The proposed method is applied over 305 region of interest (ROI) cropped from MIAS dataset. The simple logistic classifier achieved 86.66% classification accuracy rate with sensitivity 76.53% and specificity 91.3%.

  8. Intraductal papillomas on core biopsy can be upgraded to malignancy on subsequent excisional biopsy regardless of the presence of atypical features.

    PubMed

    Shiino, Sho; Tsuda, Hitoshi; Yoshida, Masayuki; Jimbo, Kenjiro; Asaga, Sota; Hojo, Takashi; Kinoshita, Takayuki

    2015-06-01

    Intraductal papillary lesions of the breast constitute a heterogeneous entity, including benign intraductal papilloma (IDP) with or without atypia and malignant papillary carcinoma. Differentiating between these diagnoses can be challenging. We re-evaluated core biopsy specimens that were diagnosed as IDP and the corresponding surgical excision specimens, and assessed the potential risk for the diagnosis to be modified to malignancy based on excision. By sorting the pathology database of the National Cancer Center Hospital, Tokyo, we identified 146 core biopsy cases that were histologically diagnosed as IDP between 1997 and 2013. The re-evaluated diagnosis was IDP without atypia in 79 (54%) patients, IDP with atypia in 66 (45%), and ductal carcinoma in situ (DCIS) in 1 (1%). Among the 34 patients (23%) who underwent surgical excision subsequent to core biopsy, histological diagnosis was upgraded to carcinoma, excluding lobular carcinoma in situ (LCIS), in 14 (41%) cases, including 4 (33%) of 12 IDPs without atypia and 10 (45%) of 22 IDPs with atypia. Complete surgical excision should be kept in mind for all IDPs diagnosed on core biopsy, not only IDPs with atypia but IDPs without atypia, especially when clinical or imaging diagnosis findings cannot rule out the possibility of malignancy, because papillary lesions comprise a variety of morphological appearances.

  9. 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.

  10. 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.

  11. Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities

    PubMed Central

    Itakura, Haruka; Achrol, Achal S.; Mitchell, Lex A.; Loya, Joshua J.; Liu, Tiffany; Westbroek, Erick M.; Feroze, Abdullah H.; Rodriguez, Scott; Echegaray, Sebastian; Azad, Tej D.; Yeom, Kristen W.; Napel, Sandy; Rubin, Daniel L.; Chang, Steven D.; Harsh, Griffith R.; Gevaert, Olivier

    2015-01-01

    Glioblastoma (GBM) is the most common and highly lethal primary malignant brain tumor in adults. There is a dire need for easily accessible, noninvasive biomarkers that can delineate underlying molecular activities and predict response to therapy. To this end, we sought to identify subtypes of GBM, differentiated solely by quantitative MR imaging features, that could be used for better management of GBM patients. Quantitative image features capturing the shape, texture, and edge sharpness of each lesion were extracted from MR images of 121 patients with de novo, solitary, unilateral GBM. Three distinct phenotypic “clusters” emerged in the development cohort using consensus clustering with 10,000 iterations on these image features. These three clusters—pre-multifocal, spherical, and rim-enhancing, names reflecting their image features—were validated in an independent cohort consisting of 144 multi-institution patients with similar tumor characteristics from The Cancer Genome Atlas (TCGA). Each cluster mapped to a unique set of molecular signaling pathways using pathway activity estimates derived from analysis of TCGA tumor copy number and gene expression data with the PARADIGM algorithm. Distinct pathways, such as c-Kit and FOXA, were enriched in each cluster, indicating differential molecular activities as determined by image features. Each cluster also demonstrated differential probabilities of survival, indicating prognostic importance. Our imaging method offers a noninvasive approach to stratify GBM patients and also provides unique sets of molecular signatures to inform targeted therapy and personalized treatment of GBM. PMID:26333934

  12. Regional shape-based feature space for segmenting biomedical images using neural networks

    NASA Astrophysics Data System (ADS)

    Sundaramoorthy, Gopal; Hoford, John D.; Hoffman, Eric A.

    1993-07-01

    In biomedical images, structure of interest, particularly the soft tissue structures, such as the heart, airways, bronchial and arterial trees often have grey-scale and textural characteristics similar to other structures in the image, making it difficult to segment them using only gray- scale and texture information. However, these objects can be visually recognized by their unique shapes and sizes. In this paper we discuss, what we believe to be, a novel, simple scheme for extracting features based on regional shapes. To test the effectiveness of these features for image segmentation (classification), we use an artificial neural network and a statistical cluster analysis technique. The proposed shape-based feature extraction algorithm computes regional shape vectors (RSVs) for all pixels that meet a certain threshold criteria. The distance from each such pixel to a boundary is computed in 8 directions (or in 26 directions for a 3-D image). Together, these 8 (or 26) values represent the pixel's (or voxel's) RSV. All RSVs from an image are used to train a multi-layered perceptron neural network which uses these features to 'learn' a suitable classification strategy. To clearly distinguish the desired object from other objects within an image, several examples from inside and outside the desired object are used for training. Several examples are presented to illustrate the strengths and weaknesses of our algorithm. Both synthetic and actual biomedical images are considered. Future extensions to this algorithm are also discussed.

  13. Use of the ThinPrep® Imaging System does not alter the frequency of interpreting Papanicolaou tests as atypical squamous cells of undetermined significance

    PubMed Central

    Thrall, Michael J; Russell, Donna K; Bonfiglio, Thomas A; Hoda, Rana S

    2008-01-01

    Background Automated screening of Papanicolaou tests (Pap tests) improves the productivity of cytopathology laboratories. The ThinPrep® Imaging System (TIS) has been widely adopted primarily for this reason for use on ThinPrep® Pap tests (TPPT). However, TIS may also influence the interpretation of Pap tests, leading to changes in the frequency of various interpretive categories. The effect of the TIS on rates of TPPT interpretation as atypical squamous cells of undetermined significance (ASC-US) is of concern because any shift in the frequency of ASC-US will alter the sensitivity and specificity of the Pap test. We have sought to determine whether automated screening of TPPT has altered ASC-US rates in our institution when compared with manual screening (MS) of TPPT. Methods A computerized search for all ASC-US with reflex Human Papillomavirus (HPV) testing over a one-year-period (7/1/06 to 6/30/07) was conducted. Cases included both TPPT screened utilizing TIS and screened manually. HPV test results for both groups were recorded. Pertinent follow-up cervical cytology and histology results were retrieved for the period extending to 11/30/07. Automated screening was in clinical use for 10 months prior to the start of the study. Results Automated screening was performed on 23,103 TPPT, of which 977 (4.23%) were interpreted as ASC-US. Over the same period, MS was performed on 45,789 TPPT, of which 1924 (4.20%) were interpreted as ASC-US. Reflex HPV testing was positive for high risk (HR) types in 47.4% of the TIS cases and 50.2% of MS cases. Follow-up cervical dysplasia found by colposcopy was also distributed proportionally between the two groups. Cervical intraepithelial neoplasia (CIN) was found on follow-up biopsy of 20.1% of the TIS cases (5.2% CIN 2/3) and 21.2% of MS cases (5.1% CIN 2/3). None of these differences were statistically significant. Conclusion Use of the ThinPrep® Imaging System did not appreciably change ASC-US rates or follow-up reflex HPV

  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 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

  15. 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.

  16. 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.

  17. Asymmetries in the direction of saccades during perception of scenes and fractals: effects of image type and image features.

    PubMed

    Foulsham, Tom; Kingstone, Alan

    2010-04-07

    The direction in which people tend to move their eyes when inspecting images can reveal the different influences on eye guidance in scene perception, and their time course. We investigated biases in saccade direction during a memory-encoding task with natural scenes and computer-generated fractals. Images were rotated to disentangle egocentric and image-based guidance. Saccades in fractals were more likely to be horizontal, regardless of orientation. In scenes, the first saccade often moved down and subsequent eye movements were predominantly vertical, relative to the scene. These biases were modulated by the distribution of visual features (saliency and clutter) in the scene. The results suggest that image orientation, visual features and the scene frame-of-reference have a rapid effect on eye guidance.

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

    PubMed

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

    2015-10-01

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

  19. 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

  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. Learning with distribution of optimized features for recognizing common CT imaging signs of lung diseases

    NASA Astrophysics Data System (ADS)

    Ma, Ling; Liu, Xiabi; Fei, Baowei

    2017-01-01

    Common CT imaging signs of lung diseases (CISLs) are defined as the imaging signs that frequently appear in lung CT images from patients. CISLs play important roles in the diagnosis of lung diseases. This paper proposes a novel learning method, namely learning with distribution of optimized feature (DOF), to effectively recognize the characteristics of CISLs. We improve the classification performance by learning the optimized features under different distributions. Specifically, we adopt the minimum spanning tree algorithm to capture the relationship between features and discriminant ability of features for selecting the most important features. To overcome the problem of various distributions in one CISL, we propose a hierarchical learning method. First, we use an unsupervised learning method to cluster samples into groups based on their distribution. Second, in each group, we use a supervised learning method to train a model based on their categories of CISLs. Finally, we obtain multiple classification decisions from multiple trained models and use majority voting to achieve the final decision. The proposed approach has been implemented on a set of 511 samples captured from human lung CT images and achieves a classification accuracy of 91.96%. The proposed DOF method is effective and can provide a useful tool for computer-aided diagnosis of lung diseases on CT images.

  2. a Modified Stochastic Neighbor Embedding for Combining Multiple Features for Remote Sensing Image Classification

    NASA Astrophysics Data System (ADS)

    Zhang, L.; Zhang, L.; Tao, D.; Huang, X.

    2012-07-01

    In remote sensing image interpretation, it is important to combine multiple features of a certain pixel in both spatial and spectral domains to improve the classification accuracy, such as spectral signature, morphological property, and shape feature. Therefore, it is essential to consider the complementary property of different features and combine them in order to obtain an accurate classification rate. In this paper, we introduce a multi-feature dimension reduction algorithm under a probabilistic framework, modified stochastic neighbor embedding (MSNE). For each feature, a probability distribution is constructed based on SNE, and then we alternatively solve SNE and learn the optimal combination coefficients for different features in optimization. Compared with conventional dimension reduction strategies, the suggested algorithm can considers spectral, morphological and shape features of a pixel to achieve a physically meaningful low-dimensional feature representation by automatically learn a combination coefficient for each feature adapted to its contribution to subsequent classification. In experimental section, classification results using hyperspectral remote sensing image (HSI) show that this modified stochastic neighbor embedding can effectively improve classification performance.

  3. Sketch-Based Image Retrieval: Benchmark and Bag-of-Features Descriptors.

    PubMed

    Eitz, M; Hildebrand, K; Boubekeur, T; Alexa, M

    2011-11-01

    We introduce a benchmark for evaluating the performance of large-scale sketch-based image retrieval systems. The necessary data are acquired in a controlled user study where subjects rate how well given sketch/image pairs match. We suggest how to use the data for evaluating the performance of sketch-based image retrieval systems. The benchmark data as well as the large image database are made publicly available for further studies of this type. Furthermore, we develop new descriptors based on the bag-of-features approach and use the benchmark to demonstrate that they significantly outperform other descriptors in the literature.

  4. Volumetric texture features from higher-order images for diagnosis of colon lesions via CT colonography

    PubMed Central

    Song, Bowen; Zhang, Guopeng; Lu, Hongbing; Wang, Huafeng; Zhu, Wei; Pickhardt, Perry J.

    2014-01-01

    Purpose Differentiation of colon lesions according to underlying pathology, e.g., neoplastic and non-neoplastic lesions, is of fundamental importance for patient management. Image intensity-based textural features have been recognized as useful biomarker for the differentiation task. In this paper, we introduce texture features from higher-order images, i.e., gradient and curvature images, beyond the intensity image, for that task. Methods Based on the Haralick texture analysis method, we introduce a virtual pathological model to explore the utility of texture features from high-order differentiations, i.e., gradient and curvature, of the image intensity distribution. The texture features were validated on a database consisting of 148 colon lesions, of which 35 are non-neoplastic lesions, using the support vector machine classifier and the merit of area under the curve (AUC) of the receiver operating characteristics. Results The AUC of classification was improved from 0.74 (using the image intensity alone) to 0.85 (by also considering the gradient and curvature images) in differentiating the neoplastic lesions from non-neoplastic ones, e.g., hyperplastic polyps from tubular adenomas, tubulovillous adenomas and adenocarcinomas. Conclusions The experimental results demonstrated that texture features from higher-order images can significantly improve the classification accuracy in pathological differentiation of colorectal lesions. The gain in differentiation capability shall increase the potential of computed tomography colonography for colorectal cancer screening by not only detecting polyps but also classifying them for optimal polyp management for the best outcome in personalized medicine. PMID:24696313

  5. Study on image feature extraction and classification for human colorectal cancer using optical coherence tomography

    NASA Astrophysics Data System (ADS)

    Huang, Shu-Wei; Yang, Shan-Yi; Huang, Wei-Cheng; Chiu, Han-Mo; Lu, Chih-Wei

    2011-06-01

    Most of the colorectal cancer has grown from the adenomatous polyp. Adenomatous lesions have a well-documented relationship to colorectal cancer in previous studies. Thus, to detect the morphological changes between polyp and tumor can allow early diagnosis of colorectal cancer and simultaneous removal of lesions. OCT (Optical coherence tomography) has been several advantages including high resolution and non-invasive cross-sectional image in vivo. In this study, we investigated the relationship between the B-scan OCT image features and histology of malignant human colorectal tissues, also en-face OCT image and the endoscopic image pattern. The in-vitro experiments were performed by a swept-source optical coherence tomography (SS-OCT) system; the swept source has a center wavelength at 1310 nm and 160nm in wavelength scanning range which produced 6 um axial resolution. In the study, the en-face images were reconstructed by integrating the axial values in 3D OCT images. The reconstructed en-face images show the same roundish or gyrus-like pattern with endoscopy images. The pattern of en-face images relate to the stages of colon cancer. Endoscopic OCT technique would provide three-dimensional imaging and rapidly reconstruct en-face images which can increase the speed of colon cancer diagnosis. Our results indicate a great potential for early detection of colorectal adenomas by using the OCT imaging.

  6. Face Recognition for Access Control Systems Combining Image-Difference Features Based on a Probabilistic Model

    NASA Astrophysics Data System (ADS)

    Miwa, Shotaro; Kage, Hiroshi; Hirai, Takashi; Sumi, Kazuhiko

    We propose a probabilistic face recognition algorithm for Access Control System(ACS)s. Comparing with existing ACSs using low cost IC-cards, face recognition has advantages in usability and security that it doesn't require people to hold cards over scanners and doesn't accept imposters with authorized cards. Therefore face recognition attracts more interests in security markets than IC-cards. But in security markets where low cost ACSs exist, price competition is important, and there is a limitation on the quality of available cameras and image control. Therefore ACSs using face recognition are required to handle much lower quality images, such as defocused and poor gain-controlled images than high security systems, such as immigration control. To tackle with such image quality problems we developed a face recognition algorithm based on a probabilistic model which combines a variety of image-difference features trained by Real AdaBoost with their prior probability distributions. It enables to evaluate and utilize only reliable features among trained ones during each authentication, and achieve high recognition performance rates. The field evaluation using a pseudo Access Control System installed in our office shows that the proposed system achieves a constant high recognition performance rate independent on face image qualities, that is about four times lower EER (Equal Error Rate) under a variety of image conditions than one without any prior probability distributions. On the other hand using image difference features without any prior probabilities are sensitive to image qualities. We also evaluated PCA, and it has worse, but constant performance rates because of its general optimization on overall data. Comparing with PCA, Real AdaBoost without any prior distribution performs twice better under good image conditions, but degrades to a performance as good as PCA under poor image conditions.

  7. Feature Selection Based on High Dimensional Model Representation for Hyperspectral Images.

    PubMed

    Taskin Kaya, Gulsen; Kaya, Huseyin; Bruzzone, Lorenzo

    2017-03-24

    In hyperspectral image analysis, the classification task has generally been addressed jointly with dimensionality reduction due to both the high correlation between the spectral features and the noise present in spectral bands which might significantly degrade classification performance. In supervised classification, limited training instances in proportion to the number of spectral features have negative impacts on the classification accuracy, which has known as Hughes effects or curse of dimensionality in the literature. In this paper, we focus on dimensionality reduction problem, and propose a novel feature-selection algorithm which is based on the method called High Dimensional Model Representation. The proposed algorithm is tested on some toy examples and hyperspectral datasets in comparison to conventional feature-selection algorithms in terms of classification accuracy, stability of the selected features and computational time. The results showed that the proposed approach provides both high classification accuracy and robust features with a satisfactory computational time.

  8. New feature extraction method for classification of agricultural products from x-ray images

    NASA Astrophysics Data System (ADS)

    Talukder, Ashit; Casasent, David P.; Lee, Ha-Woon; Keagy, Pamela M.; Schatzki, Thomas F.

    1999-01-01

    Classification of real-time x-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a system for automated non- invasive detection of defective product items on a conveyor belt. We discuss the extraction of new features that allow better discrimination between damaged and clean items. This feature extraction and classification stage is the new aspect of this paper; our new maximum representation and discrimination between damaged and clean items. This feature extraction and classification stage is the new aspect of this paper; our new maximum representation and discriminating feature (MRDF) extraction method computes nonlinear features that are used as inputs to a new modified k nearest neighbor classifier. In this work the MRDF is applied to standard features. The MRDF is robust to various probability distributions of the input class and is shown to provide good classification and new ROC data.

  9. 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

  10. 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.

  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. 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 %.

  13. Research on texture feature of RS image based on cloud model

    NASA Astrophysics Data System (ADS)

    Wang, Zuocheng; Xue, Lixia

    2008-10-01

    This paper presents a new method applied to texture feature representation in RS image based on cloud model. Aiming at the fuzziness and randomness of RS image, we introduce the cloud theory into RS image processing in a creative way. The digital characteristics of clouds well integrate the fuzziness and randomness of linguistic terms in a unified way and map the quantitative and qualitative concepts. We adopt texture multi-dimensions cloud to accomplish vagueness and randomness handling of texture feature in RS image. The method has two steps: 1) Correlativity analyzing of texture statistical parameters in Grey Level Co-occurrence Matrix (GLCM) and parameters fuzzification. GLCM can be used to representing the texture feature in many aspects perfectly. According to the expressive force of texture statistical parameters and by Correlativity analyzing of texture statistical parameters, we can abstract a few texture statistical parameters that can best represent the texture feature. By the fuzziness algorithm, the texture statistical parameters can be mapped to fuzzy cloud space. 2) Texture multi-dimensions cloud model constructing. Based on the abstracted texture statistical parameters and fuzziness cloud space, texture multi-dimensions cloud model can be constructed in micro-windows of image. According to the membership of texture statistical parameters, we can achieve the samples of cloud-drop. By backward cloud generator, the digital characteristics of texture multi-dimensions cloud model can be achieved and the Mathematical Expected Hyper Surface(MEHS) of multi-dimensions cloud of micro-windows can be constructed. At last, the weighted sum of the 3 digital characteristics of micro-window cloud model was proposed and used in texture representing in RS image. The method we develop is demonstrated by applying it to texture representing in many RS images, various performance studies testify that the method is both efficient and effective. It enriches the cloud

  14. WE-G-207-05: Relationship Between CT Image Quality, Segmentation Performance, and Quantitative Image Feature Analysis

    SciTech Connect

    Lee, J; Nishikawa, R; Reiser, I; Boone, J

    2015-06-15

    Purpose: Segmentation quality can affect quantitative image feature analysis. The objective of this study is to examine the relationship between computed tomography (CT) image quality, segmentation performance, and quantitative image feature analysis. Methods: A total of 90 pathology proven breast lesions in 87 dedicated breast CT images were considered. An iterative image reconstruction (IIR) algorithm was used to obtain CT images with different quality. With different combinations of 4 variables in the algorithm, this study obtained a total of 28 different qualities of CT images. Two imaging tasks/objectives were considered: 1) segmentation and 2) classification of the lesion as benign or malignant. Twenty-three image features were extracted after segmentation using a semi-automated algorithm and 5 of them were selected via a feature selection technique. Logistic regression was trained and tested using leave-one-out-cross-validation and its area under the ROC curve (AUC) was recorded. The standard deviation of a homogeneous portion and the gradient of a parenchymal portion of an example breast were used as an estimate of image noise and sharpness. The DICE coefficient was computed using a radiologist’s drawing on the lesion. Mean DICE and AUC were used as performance metrics for each of the 28 reconstructions. The relationship between segmentation and classification performance under different reconstructions were compared. Distributions (median, 95% confidence interval) of DICE and AUC for each reconstruction were also compared. Results: Moderate correlation (Pearson’s rho = 0.43, p-value = 0.02) between DICE and AUC values was found. However, the variation between DICE and AUC values for each reconstruction increased as the image sharpness increased. There was a combination of IIR parameters that resulted in the best segmentation with the worst classification performance. Conclusion: There are certain images that yield better segmentation or classification

  15. 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.

  16. Atypical white-matter microstructure in congenitally deaf adults: A region of interest and tractography study using diffusion-tensor imaging.

    PubMed

    Karns, Christina M; Stevens, Courtney; Dow, Mark W; Schorr, Emily M; Neville, Helen J

    2017-01-01

    Considerable research documents the cross-modal reorganization of auditory cortices as a consequence of congenital deafness, with remapped functions that include visual and somatosensory processing of both linguistic and nonlinguistic information. Structural changes accompany this cross-modal neuroplasticity, but precisely which specific structural changes accompany congenital and early deafness and whether there are group differences in hemispheric asymmetries remain to be established. Here, we used diffusion tensor imaging (DTI) to examine microstructural white matter changes accompanying cross-modal reorganization in 23 deaf adults who were genetically, profoundly, and congenitally deaf, having learned sign language from infancy with 26 hearing controls who participated in our previous fMRI studies of cross-modal neuroplasticity. In contrast to prior literature using a whole-brain approach, we introduce a semiautomatic method for demarcating auditory regions in which regions of interest (ROIs) are defined on the normalized white matter skeleton for all participants, projected into each participants native space, and manually constrained to anatomical boundaries. White-matter ROIs were left and right Heschl's gyrus (HG), left and right anterior superior temporal gyrus (aSTG), left and right posterior superior temporal gyrus (pSTG), as well as one tractography-defined region in the splenium of the corpus callosum connecting homologous left and right superior temporal regions (pCC). Within these regions, we measured fractional anisotropy (FA), radial diffusivity (RD), axial diffusivity (AD), and white-matter volume. Congenitally deaf adults had reduced FA and volume in white matter structures underlying bilateral HG, aSTG, pSTG, and reduced FA in pCC. In HG and pCC, this reduction in FA corresponded with increased RD, but differences in aSTG and pSTG could not be localized to alterations in RD or AD. Direct statistical tests of hemispheric asymmetries in these

  17. Assessing Agreement between Radiomic Features Computed for Multiple CT Imaging Settings

    PubMed Central

    Lu, Lin; Ehmke, Ross C.; Schwartz, Lawrence H.; Zhao, Binsheng

    2016-01-01

    Objectives Radiomics utilizes quantitative image features (QIFs) to characterize tumor phenotype. In practice, radiological images are obtained from different vendors’ equipment using various imaging acquisition settings. Our objective was to assess the inter-setting agreement of QIFs computed from CT images by varying two parameters, slice thickness and reconstruction algorithm. Materials and Methods CT images from an IRB-approved/HIPAA-compliant study assessing thirty-two lung cancer patients were included for the analysis. Each scan’s raw data were reconstructed into six imaging series using combinations of two reconstruction algorithms (Lung[L] and Standard[S]) and three slice thicknesses (1.25mm, 2.5mm and 5mm), i.e., 1.25L, 1.25S, 2.5L, 2.5S, 5L and 5S. For each imaging-setting, 89 well-defined QIFs were computed for each of the 32 tumors (one tumor per patient). The six settings led to 15 inter-setting comparisons (combinatorial pairs). To reduce QIF redundancy, hierarchical clustering was done. Concordance correlation coefficients (CCCs) were used to assess inter-setting agreement of the non-redundant feature groups. The CCC of each group was assessed by averaging CCCs of QIFs in the group. Results Twenty-three non-redundant feature groups were created. Across all feature groups, the best inter-setting agreements (CCCs>0.8) were 1.25S vs 2.5S, 1.25L vs 2.5L, and 2.5S vs 5S; the worst (CCCs<0.51) belonged to 1.25L vs 5S and 2.5L vs 5S. Eight of the feature groups related to size, shape, and coarse texture had an average CCC>0.8 across all imaging settings. Conclusions Varying degrees of inter-setting disagreements of QIFs exist when features are computed from CT images reconstructed using different algorithms and slice thicknesses. Our findings highlight the importance of harmonizing imaging acquisition for obtaining consistent QIFs to study tumor imaging phonotype. PMID:28033372

  18. Adaptive semisupervised feature selection without graph construction for very-high-resolution remote sensing images

    NASA Astrophysics Data System (ADS)

    Chen, Xi; Qi, Jinzi; Chen, Yushi; Hua, Lizhong; Shao, Guofan

    2016-04-01

    Semisupervised feature selection methods can improve classification performance and enhance model comprehensibility with few labeled objects. However, most of the existing methods require graph construction beforehand, and the resulting heavy computational cost may bring about the failure to accurately capture the local geometry of data. To overcome the problem, adaptive semisupervised feature selection (ASFS) is proposed. In ASFS, the goodness of each feature is measured by linear objective functions based on loss functions and probability distribution matrices. By alternatively optimizing model parameters and automatically adjusting the probabilities of boundary objects, ASFS can measure the genuine characteristics of the data and then rank and select features. The experimental results attest to the effectiveness and practicality of the method in comparison with the latest and state-of-the-art methods on a Worldview II image and a Quickbird II image.

  19. Automatic segmentation of closed-contour features in ophthalmic images using graph theory and dynamic programming.

    PubMed

    Chiu, Stephanie J; Toth, Cynthia A; Bowes Rickman, Catherine; Izatt, Joseph A; Farsiu, Sina

    2012-05-01

    This paper presents a generalized framework for segmenting closed-contour anatomical and pathological features using graph theory and dynamic programming (GTDP). More specifically, the GTDP method previously developed for quantifying retinal and corneal layer thicknesses is extended to segment objects such as cells and cysts. The presented technique relies on a transform that maps closed-contour features in the Cartesian domain into lines in the quasi-polar domain. The features of interest are then segmented as layers via GTDP. Application of this method to segment closed-contour features in several ophthalmic image types is shown. Quantitative validation experiments for retinal pigmented epithelium cell segmentation in confocal fluorescence microscopy images attests to the accuracy of the presented technique.

  20. Classification of pulmonary nodules in lung CT images using shape and texture features

    NASA Astrophysics Data System (ADS)

    Dhara, Ashis Kumar; Mukhopadhyay, Sudipta; Dutta, Anirvan; Garg, Mandeep; Khandelwal, Niranjan; Kumar, Prafulla

    2016-03-01

    Differentiation of malignant and benign pulmonary nodules is important for prognosis of lung cancer. In this paper, benign and malignant nodules are classified using support vector machine. Several shape-based and texture-based features are used to represent the pulmonary nodules in the feature space. A semi-automated technique is used for nodule segmentation. Relevant features are selected for efficient representation of nodules in the feature space. The proposed scheme and the competing technique are evaluated on a data set of 542 nodules of Lung Image Database Consortium and Image Database Resource Initiative. The nodules with composite rank of malignancy "1","2" are considered as benign and "4","5" are considered as malignant. Area under the receiver operating characteristics curve is 0:9465 for the proposed method. The proposed method outperforms the competing technique.

  1. 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.

  2. 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.

  3. An atypical monomelic presentation of Mazabraud syndrome

    PubMed Central

    Wan, Jun; He, Hong-Bo; Liao, Qian-De; Zhang, Can

    2014-01-01

    Mazabraud syndrome is a rare condition characterized by a combination of fibrous dysplasia and intramuscular myxomas. In Mazabraud syndrome, the distribution of fibrous dysplasia is mostly polyomelic and frequently located in the femur, with myxomas adjacent to the fibrous dysplasia lesion of bone (mostly in the quadriceps muscle). However, when presented as atypical clinical features, patients of Mazabraud syndrome is either misdiagnosed or difficult to diagnose. We report an atypical monomelic case of Mazabraud syndrome in the right upper arm and discuss the difficulties in making an accurate diagnosis. PMID:25143651

  4. 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.

  5. Synchrotron imaging and diffraction to investigate internal features of stable tearing fracture phenomenon in metallic specimens.

    PubMed

    Frink, Elizabeth; Lease, Kevin

    2012-07-01

    In situ synchrotron imaging and diffraction on beamline 1-ID-C at the Advanced Photon Source (APS) has been used to investigate the internal features present during the stable tearing fracture phenomenon in low-constraint metallic specimens. The results that are obtained from this investigation show that the internal features are identifiable with these techniques, and this initial investigation lays the groundwork for future, more in-depth investigations with some improvements to the methods.

  6. Identity Recognition Algorithm Using Improved Gabor Feature Selection of Gait Energy Image

    NASA Astrophysics Data System (ADS)

    Chao, LIANG; Ling-yao, JIA; Dong-cheng, SHI

    2017-01-01

    This paper describes an effective gait recognition approach based on Gabor features of gait energy image. In this paper, the kernel Fisher analysis combined with kernel matrix is proposed to select dominant features. The nearest neighbor classifier based on whitened cosine distance is used to discriminate different gait patterns. The approach proposed is tested on the CASIA and USF gait databases. The results show that our approach outperforms other state of gait recognition approaches in terms of recognition accuracy and robustness.

  7. When age-progressed images are unreliable: The roles of external features and age range.

    PubMed

    Erickson, William Blake; Lampinen, James Michael; Frowd, Charlie D; Mahoney, Gregory

    2017-03-01

    When children go missing for many years, investigators commission age-progressed images from forensic artists to depict an updated appearance. These images have anecdotal success, and systematic research has found they lead to accurate recognition rates comparable to outdated photos. The present study examines the reliability of age progressions of the same individuals created by different artists. Eight artists first generated age progressions of eight targets across three age ranges. Eighty-five participants then evaluated the similarity of these images against other images depicting the same targets progressed at the same age ranges, viewing either whole faces or faces with external features concealed. Similarities were highest over shorter age ranges and when external features were concealed. Implications drawn from theory and application are discussed.

  8. Statistical multiscale blob features for classifying and retrieving image texture from large-scale databases

    NASA Astrophysics Data System (ADS)

    Xu, Qi; Wu, Haishan; Chen, Yan Qiu

    2010-10-01

    The extraction of texture features from images faces two new challenges: large-scale databases with diversified textures, and varying imaging conditions. We propose a novel method termed multiscale blob features (MBF) to overcome these two difficulties. MBF analyzes textures in both resolution scale and gray level. Proposed statistical descriptors effectively extract structural information from the decomposed binary images. Experimental results show that MBF outperforms other methods on combined large-scale databases (VisTex+Brodatz+CUReT+OuTex). Moreover, experimental results on the University of Illinois at Urbana-Champaign database and the entire Brodatz's atlas show that MBF is invariant to gray-level scaling and image rotation, and is robust across a substantial range of spatial scaling.

  9. 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.

  10. 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.

  11. 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.

  12. An effective image classification method with the fusion of invariant feature and a new color descriptor

    NASA Astrophysics Data System (ADS)

    Mansourian, Leila; Taufik Abdullah, Muhamad; Nurliyana Abdullah, Lili; Azman, Azreen; Mustaffa, Mas Rina

    2017-02-01

    Pyramid Histogram of Words (PHOW), combined Bag of Visual Words (BoVW) with the spatial pyramid matching (SPM) in order to add location information to extracted features. However, different PHOW extracted from various color spaces, and they did not extract color information individually, that means they discard color information, which is an important characteristic of any image that is motivated by human vision. This article, concatenated PHOW Multi-Scale Dense Scale Invariant Feature Transform (MSDSIFT) histogram and a proposed Color histogram to improve the performance of existing image classification algorithms. Performance evaluation on several datasets proves that the new approach outperforms other existing, state-of-the-art methods.

  13. Contrast in Terahertz Images of Archival Documents—Part II: Influence of Topographic Features

    NASA Astrophysics Data System (ADS)

    Bardon, Tiphaine; May, Robert K.; Taday, Philip F.; Strlič, Matija

    2017-04-01

    We investigate the potential of terahertz time-domain imaging in reflection mode to reveal archival information in documents in a non-invasive way. In particular, this study explores the parameters and signal processing tools that can be used to produce well-contrasted terahertz images of topographic features commonly found in archival documents, such as indentations left by a writing tool, as well as sieve lines. While the amplitude of the waveforms at a specific time delay can provide the most contrasted and legible images of topographic features on flat paper or parchment sheets, this parameter may not be suitable for documents that have a highly irregular surface, such as water- or fire-damaged documents. For analysis of such documents, cross-correlation of the time-domain signals can instead yield images with good contrast. Analysis of the frequency-domain representation of terahertz waveforms can also provide well-contrasted images of topographic features, with improved spatial resolution when utilising high-frequency content. Finally, we point out some of the limitations of these means of analysis for extracting information relating to topographic features of interest from documents.

  14. Contrast in Terahertz Images of Archival Documents—Part II: Influence of Topographic Features

    NASA Astrophysics Data System (ADS)

    Bardon, Tiphaine; May, Robert K.; Taday, Philip F.; Strlič, Matija

    2017-01-01

    We investigate the potential of terahertz time-domain imaging in reflection mode to reveal archival information in documents in a non-invasive way. In particular, this study explores the parameters and signal processing tools that can be used to produce well-contrasted terahertz images of topographic features commonly found in archival documents, such as indentations left by a writing tool, as well as sieve lines. While the amplitude of the waveforms at a specific time delay can provide the most contrasted and legible images of topographic features on flat paper or parchment sheets, this parameter may not be suitable for documents that have a highly irregular surface, such as water- or fire-damaged documents. For analysis of such documents, cross-correlation of the time-domain signals can instead yield images with good contrast. Analysis of the frequency-domain representation of terahertz waveforms can also provide well-contrasted images of topographic features, with improved spatial resolution when utilising high-frequency content. Finally, we point out some of the limitations of these means of analysis for extracting information relating to topographic features of interest from documents.

  15. Automated hand thermal image segmentation and feature extraction in the evaluation of rheumatoid arthritis.

    PubMed

    Snekhalatha, U; Anburajan, M; Sowmiya, V; Venkatraman, B; Menaka, M

    2015-04-01

    The aim of the study was (1) to perform an automated segmentation of hot spot regions of the hand from thermograph using the k-means algorithm and (2) to test the potential of features extracted from the hand thermograph and its measured skin temperature indices in the evaluation of rheumatoid arthritis. Thermal image analysis based on skin temperature measurement, heat distribution index and thermographic index was analyzed in rheumatoid arthritis patients and controls. The k-means algorithm was used for image segmentation, and features were extracted from the segmented output image using the gray-level co-occurrence matrix method. In metacarpo-phalangeal, proximal inter-phalangeal and distal inter-phalangeal regions, the calculated percentage difference in the mean values of skin temperatures was found to be higher in rheumatoid arthritis patients (5.3%, 4.9% and 4.8% in MCP3, PIP3 and DIP3 joints, respectively) as compared to the normal group. k-Means algorithm applied in the thermal imaging provided better segmentation results in evaluating the disease. In the total population studied, the measured mean average skin temperature of the MCP3 joint was highly correlated with most of the extracted features of the hand. In the total population studied, the statistical feature extracted parameters correlated significantly with skin surface temperature measurements and measured temperature indices. Hence, the developed computer-aided diagnostic tool using MATLAB could be used as a reliable method in diagnosing and analyzing the arthritis in hand thermal images.

  16. Classification of photographed document images based on deep-learning features

    NASA Astrophysics Data System (ADS)

    Zhong, Guoqiang; Yao, Hui; Liu, Yutong; Hong, Chen; Pham, Tuan

    2017-02-01

    In this paper, we propose two new problems related to classification of photographed document images, and based on deep learning methods, present the baseline solutions for these two problems. The first problem is that, for some photographed document images, which book do they belong to? The second one is, for some photographed document images, what is the type of the book they belong to? To address these two problems, we apply "AexNet" to the collected document images. Using the pre-trained "AlexNet" on the ImageNet data set directly, we obtain 92.57% accuracy for the book-name classification and 93.33% accuracy for the book-type one. After fine-tuning on the training set of the photographed document images, the accuracy of the book-name classification increases to 95.54% and that of the booktype one to 95.42%. To our best knowledge, although there exist many image classification algorithm, no previous work has targeted to these two challenging problems. In addition, the experiments demonstrate that deep-learning features outperform features extracted with traditional image descriptors on these two problems.

  17. Feature based nonrigid brain MR image registration with symmetric alpha stable filters.

    PubMed

    Liao, Shu; Chung, Albert C S

    2010-01-01

    A new feature based nonrigid image registration method for magnetic resonance (MR) brain images is presented in this paper. Each image voxel is represented by a rotation invariant feature vector, which is computed by passing the input image volumes through a new bank of symmetric alpha stable (SalphaS) filters. There are three main contributions presented in this paper. First, this work is motivated by the fact that the frequency spectrums of the brain MR images often exhibit non-Gaussian heavy-tail behavior which cannot be satisfactorily modeled by the conventional Gabor filters. To this end, we propose the use of SalphaS filters to model such behavior and show that the Gabor filter is a special case of the SalphaS filter. Second, the maximum response orientation (MRO) selection criterion is designed to extract rotation invariant features for registration tasks. The MRO selection criterion also significantly reduces the number of dimensions of feature vectors and therefore lowers the computation time. Third, in case the segmentations of the input image volumes are available, the Fisher's separation criterion (FSC) is introduced such that the discriminating power of different feature types can be directly compared with each other before performing the registration process. Using FSC, weights can also be assigned automatically to different voxels in the brain MR images. The weight of each voxel determined by FSC reflects how distinctive and salient the voxel is. Using the most distinctive and salient voxels at the initial stage to drive the registration can reduce the risk of being trapped in the local optimum during image registration process. The larger the weight, the more important the voxel. With the extracted feature vectors and the associated weights, the proposed method registers the source and the target images in a hierarchical multiresolution manner. The proposed method has been intensively evaluated on both simulated and real 3-D datasets obtained from

  18. Kernel regression based feature extraction for 3D MR image denoising.

    PubMed

    López-Rubio, Ezequiel; Florentín-Núñez, María Nieves

    2011-08-01

    Kernel regression is a non-parametric estimation technique which has been successfully applied to image denoising and enhancement in recent times. Magnetic resonance 3D image denoising has two features that distinguish it from other typical image denoising applications, namely the tridimensional structure of the images and the nature of the noise, which is Rician rather than Gaussian or impulsive. Here we propose a principled way to adapt the general kernel regression framework to this particular problem. Our noise removal system is rooted on a zeroth order 3D kernel regression, which computes a weighted average of the pixels over a regression window. We propose to obtain the weights from the similarities among small sized feature vectors associated to each pixel. In turn, these features come from a second order 3D kernel regression estimation of the original image values and gradient vectors. By considering directional information in the weight computation, this approach substantially enhances the performance of the filter. Moreover, Rician noise level is automatically estimated without any need of human intervention, i.e. our method is fully automated. Experimental results over synthetic and real images demonstrate that our proposal achieves good performance with respect to the other MRI denoising filters being compared.

  19. 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/.

  20. 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

  1. 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

  2. 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.

  3. Application of Fuzzy c-Means and Joint-Feature-Clustering to Detect Redundancies of Image-Features in Drug Combinations Studies of Breast Cancer

    NASA Astrophysics Data System (ADS)

    Brandl, Miriam B.; Beck, Dominik; Pham, Tuan D.

    2011-06-01

    The high dimensionality of image-based dataset can be a drawback for classification accuracy. In this study, we propose the application of fuzzy c-means clustering, cluster validity indices and the notation of a joint-feature-clustering matrix to find redundancies of image-features. The introduced matrix indicates how frequently features are grouped in a mutual cluster. The resulting information can be used to find data-derived feature prototypes with a common biological meaning, reduce data storage as well as computation times and improve the classification accuracy.

  4. Method for optical coherence tomography image classification using local features and earth mover's distance

    NASA Astrophysics Data System (ADS)

    Sun, Yankui; Lei, Ming

    2009-09-01

    Optical coherence tomography (OCT) is a recent imaging method that allows high-resolution, cross-sectional imaging through tissues and materials. Over the past 18 years, OCT has been successfully used in disease diagnosis, biomedical research, material evaluation, and many other domains. As OCT is a recent imaging method, until now surgeons have limited experience using it. In addition, the number of images obtained from the imaging device is too large, so we need an automated method to analyze them. We propose a novel method for automated classification of OCT images based on local features and earth mover's distance (EMD). We evaluated our algorithm using an OCT image set which contains two kinds of skin images, normal skin and nevus flammeus. Experimental results demonstrate the effectiveness of our method, which achieved classification accuracy of 0.97 for an EMD+KNN scheme and 0.99 for an EMD+SVM (support vector machine) scheme, much higher than the previous method. Our approach is especially suitable for nonhomogeneous images and could be applied to a wide range of OCT images.

  5. Medical X-ray Image Hierarchical Classification Using a Merging and Splitting Scheme in Feature Space.

    PubMed

    Fesharaki, Nooshin Jafari; Pourghassem, Hossein

    2013-07-01

    Due to the daily mass production and the widespread variation of medical X-ray images, it is necessary to classify these for searching and retrieving proposes, especially for content-based medical image retrieval systems. In this paper, a medical X-ray image hierarchical classification structure based on a novel merging and splitting scheme and using shape and texture features is proposed. In the first level of the proposed structure, to improve the classification performance, similar classes with regard to shape contents are grouped based on merging measures and shape features into the general overlapped classes. In the next levels of this structure, the overlapped classes split in smaller classes based on the classification performance of combination of shape and texture features or texture features only. Ultimately, in the last levels, this procedure is also continued forming all the classes, separately. Moreover, to optimize the feature vector in the proposed structure, we use orthogonal forward selection algorithm according to Mahalanobis class separability measure as a feature selection and reduction algorithm. In other words, according to the complexity and inter-class distance of each class, a sub-space of the feature space is selected in each level and then a supervised merging and splitting scheme is applied to form the hierarchical classification. The proposed structure is evaluated on a database consisting of 2158 medical X-ray images of 18 classes (IMAGECLEF 2005 database) and accuracy rate of 93.6% in the last level of the hierarchical structure for an 18-class classification problem is obtained.

  6. 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

  7. Exact feature extraction using finite rate of innovation principles with an application to image super-resolution.

    PubMed

    Baboulaz, Loïc; Dragotti, Pier Luigi

    2009-02-01

    The accurate registration of multiview images is of central importance in many advanced image processing applications. Image super-resolution, for example, is a typical application where the quality of the super-resolved image is degrading as registration errors increase. Popular registration methods are often based on features extracted from the acquired images. The accuracy of the registration is in this case directly related to the number of extracted features and to the precision at which the features are located: images are best registered when many features are found with a good precision. However, in low-resolution images, only a few features can be extracted and often with a poor precision. By taking a sampling perspective, we propose in this paper new methods for extracting features in low-resolution images in order to develop efficient registration techniques. We consider, in particular, the sampling theory of signals with finite rate of innovation and show that some features of interest for registration can be retrieved perfectly in this framework, thus allowing an exact registration. We also demonstrate through simulations that the sampling model which enables the use of finite rate of innovation principles is well suited for modeling the acquisition of images by a camera. Simulations of image registration and image super-resolution of artificially sampled images are first presented, analyzed and compared to traditional techniques. We finally present favorable experimental results of super-resolution of real images acquired by a digital camera available on the market.

  8. Diffusion-weighted imaging of the abdomen: Impact of b-values on texture analysis features.

    PubMed

    Becker, Anton S; Wagner, Matthias W; Wurnig, Moritz C; Boss, Andreas

    2017-01-01

    The purpose of this work was to systematically assess the impact of the b-value on texture analysis in MR diffusion-weighted imaging (DWI) of the abdomen. In eight healthy male volunteers, echo-planar DWI sequences at 16 b-values ranging between 0 and 1000 s/mm(2) were acquired at 3 T. Three different apparent diffusion coefficient (ADC) maps were computed (0, 750/100, 390, 750 s/mm(2) /all b-values). Texture analysis of rectangular regions of interest in the liver, kidney, spleen, pancreas, paraspinal muscle and subcutaneous fat was performed on DW images and the ADC maps, applying 19 features computed from the histogram, grey-level co-occurrence matrix (GLCM) and grey-level run-length matrix (GLRLM). Correlations between b-values and texture features were tested with a linear and an exponential model; the best fit was determined by the smallest sum of squared residuals. Differences between the ADC maps were assessed with an analysis of variance. A Bonferroni-corrected p-value less than 0.008 (=0.05/6) was considered statistically significant. Most GLCM and GLRLM-derived texture features (12-18 per organ) showed significant correlations with the b-value. Four texture features correlated significantly with changing b-values in all organs (p < 0.008). Correlation coefficients varied between 0.7 and 1.0. The best fit varied across different structures, with fat exhibiting mostly exponential (17 features), muscle mostly linear (12 features) and the parenchymatous organs mixed feature alterations. Two GLCM features showed significant variability in the different ADC maps. Several texture features vary systematically in healthy tissues at different b-values, which needs to be taken into account if DWI data with different b-values are analyzed. Histogram and GLRLM-derived texture features are stable on ADC maps computed from different b-values.

  9. IMAGING DIAGNOSIS-MAGNETIC RESONANCE IMAGING FEATURES OF CRANIOMANDIBULAR OSTEOPATHY IN AN AIREDALE TERRIER.

    PubMed

    Matiasovic, Matej; Caine, Abby; Scarpante, Elena; Cherubini, Giunio Bruto

    2016-05-01

    An Airedale Terrier was presented for evaluation of depression and reluctance to be touched on the head. Magnetic resonance (MR) imaging of the head was performed. The images revealed bone lesions affecting the calvarium at the level of the coronal suture and left mandibular ramus, with focal cortical destruction, expansion, and reactive new bone formation. Skull lesions were hypointense on T1-weighted sequences, hyperintense on T2-weighted sequences, and showed an intense and homogeneous enhancement after gadolinium administration. Reactive new bone formation and periosteal proliferation were confirmed histopathologically. The clinical signs, imaging findings, and histopathological examination were consistent with craniomandibular osteopathy.

  10. 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.

  11. Imaging Features of Radiofrequency Ablation with Heat-Deployed Liposomal Doxorubicin in Hepatic Tumors

    SciTech Connect

    Hong, Cheng William Chow, Lucy; Turkbey, Evrim B.; Lencioni, Riccardo; Libutti, Steven K.; Wood, Bradford J.

    2016-03-15

    IntroductionThe imaging features of unresectable hepatic malignancies in patients who underwent radiofrequency ablation (RFA) in combination with lyso-thermosensitive liposomal doxorubicin (LTLD) were determined.Materials and MethodsA phase I dose escalation study combining RFA with LTLD was performed with peri- and post- procedural CT and MRI. Imaging features were analyzed and measured in terms of ablative zone size and surrounding penumbra size. The dynamic imaging appearance was described qualitatively immediately following the procedure and at 1-month follow-up. The control group receiving liver RFA without LTLD was compared to the study group in terms of imaging features and post-ablative zone size dynamics at follow-up.ResultsPost-treatment scans of hepatic lesions treated with RFA and LTLD have distinctive imaging characteristics when compared to those treated with RFA alone. The addition of LTLD resulted in a regular or smooth enhancing rim on T1W MRI which often correlated with increased attenuation on CT. The LTLD-treated ablation zones were stable or enlarged at follow-up four weeks later in 69 % of study subjects as opposed to conventional RFA where the ablation zone underwent involution compared to imaging acquired immediately after the procedure.ConclusionThe imaging features following RFA with LTLD were different from those after standard RFA and can mimic residual or recurrent tumor. Knowledge of the subtle findings between the two groups can help avoid misinterpretation and proper identification of treatment failure in this setting. Increased size of the LTLD-treated ablation zone after RFA suggests the ongoing drug-induced biological effects.

  12. 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.

  13. Oblique low-altitude image matching using robust perspective invariant features

    NASA Astrophysics Data System (ADS)

    He, Haiqing; Du, Jing; Chen, Xiaoyong; Wang, Yuqian

    2017-01-01

    Compared with vertical photogrammtry, oblique photogrammetry is radically different for images acquired from sensor with big yaw, pitch, and roll angles. Image matching is a vital step and core problem of oblique low-altitude photogrammetric process. Among the most popular oblique images matching methods are currently SIFT/ASIFT and many affine invariant feature-based approaches, which are mainly used in computer vision, while these methods are unsuitable for requiring evenly distributed corresponding points and high efficiency simultaneously in oblique photogrammetry. In this paper, we present an oblique low-altitude images matching approach using robust perspective invariant features. Firstly, the homography matrix is estimated by a few corresponding points obtained from top pyramid images matching in several projective simulation. Then images matching are implemented by sub-pixel Harris corners and descriptors after shape perspective transforming on the basis of homography matrix. Finally, the error or gross error matched points are excluded by epipolar geometry, RANSAC algorithm and back projection constraint. Experimental results show that the proposed approach can achieve more excellent performances in oblique low-altitude images matching than the common methods, including SIFT and SURF. And the proposed approach can significantly improve the computational efficiency compared with ASIFT and Affine-SURF.

  14. 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

  15. A complete passive blind image copy-move forensics scheme based on compound statistics features.

    PubMed

    Peng, Fei; Nie, Yun-ying; Long, Min

    2011-10-10

    Since most sensor pattern noise based image copy-move forensics methods require a known reference sensor pattern noise, it generally results in non-blinded passive forensics, which significantly confines the application circumstances. In view of this, a novel passive-blind image copy-move forensics scheme is proposed in this paper. Firstly, a color image is transformed into a grayscale one, and wavelet transform based de-noising filter is used to extract the sensor pattern noise, then the variance of the pattern noise, the signal noise ratio between the de-noised image and the pattern noise, the information entropy and the average energy gradient of the original grayscale image are chosen as features, non-overlapping sliding window operations are done to the images to divide them into different sub-blocks. Finally, the tampered areas are detected by analyzing the correlation of the features between the sub-blocks and the whole image. Experimental results and analysis show that the proposed scheme is completely passive-blind, has a good detection rate, and is robust against JPEG compression, noise, rotation, scaling and blurring.

  16. 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

  17. Database for temporal events and spatial object features in time-lapse images

    NASA Astrophysics Data System (ADS)

    Eggers, Charles E.; Trivedi, Mohan M.

    2000-04-01

    We present an image database system with the capability to locate specified object-level merge and separation events in a sequence of time-lapse images. Specifically, the objects of interest are live cells in phase contrast images acquired by scanning cytometry. The system is named TERSIS and it resides on a workstation accessing time lapse images on CD- ROM. The cell objects are segmented and the resulting data are processed to extract a time series and its time derivative series for each spatial feature. Cell objects are tracked through the image sequence by applying similarity metrics to the cell object feature vectors, and cell merge and separation events are located using global image statistics. Multiple hypotheses are generated and scored to determine participating cell objects in merge/separation events. The cell association and time-varying spatial data re stored in a database. A graphical suer interface provides the user with tools to specify queries for specific cellular states and events for recall and display. Primary limitation include the need for an automatic front-end segmenter and increased cell tracking volume. The design of this system is extensible to other object types and forms of sequential image input, including video.

  18. 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.

  19. Feature-based fuzzy connectedness segmentation of ultrasound images with an object completion step.

    PubMed

    Rueda, Sylvia; Knight, Caroline L; Papageorghiou, Aris T; Noble, J Alison

    2015-12-01

    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.

  20. 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.

  1. Sensitivity of Image Features to Noise in Conventional and Respiratory-Gated PET/CT Images of Lung Cancer: Uncorrelated Noise Effects.

    PubMed

    Oliver, Jasmine A; Budzevich, Mikalai; Hunt, Dylan; Moros, Eduardo G; Latifi, Kujtim; Dilling, Thomas J; Feygelman, Vladimir; Zhang, Geoffrey

    2016-08-08

    The effect of noise on image features has yet to be studied in depth. Our objective was to explore how significantly image features are affected by the addition of uncorrelated noise to an image. The signal-to-noise ratio and noise power spectrum were calculated for a positron emission tomography/computed tomography scanner using a Ge-68 phantom. The conventional and respiratory-gated positron emission tomography/computed tomography images of 31 patients with lung cancer were retrospectively examined. Multiple sets of noise images were created for each original image by adding Gaussian noise of varying standard deviation equal to 2.5%, 4.0%, and 6.0% of the maximum intensity for positron emission tomography images and 10, 20, 50, 80, and 120 Hounsfield units for computed tomography images. Image features were extracted from all images, and percentage differences between the original image and the noise image feature values were calculated. These features were then categorized according to the noise sensitivity. The contour-dependent shape descriptors averaged below 4% difference in positron emission tomography and below 13% difference in computed tomography between noise and original images. Gray level size zone matrix features were the most sensitive to uncorrelated noise exhibiting average differences >200% for conventional and respiratory-gated images in computed tomography and 90% in positron emission tomography. Image feature differences increased as the noise level increased for shape, intensity, and gray-level co-occurrence matrix features in positron emission tomography and for gray-level co-occurrence matrix and gray-level size zone matrix features in conventional computed tomography. Investigators should be aware of the noise effects on image features.

  2. Histological Image Processing Features Induce a Quantitative Characterization of Chronic Tumor Hypoxia.

    PubMed

    Sundstrom, Andrew; 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.

  3. Clinical, electrophysiological and brain imaging features during recurrent ictal cortical blindness associated with chronic liver failure.

    PubMed

    van Pesch, V; Hernalsteen, D; van Rijckevorsel, K; Duprez, Th; Boschi, A; Ivanoiu, A; Sindic, C J M

    2006-12-01

    Transient neuroimaging features indicating primary cortical and secondary subcortical white matter cytotoxic oedema have been described in association with prolonged or intense seizures. We describe the unusual condition of recurrent ictal cortical blindness due to focal occipital status epilepticus, in the context of chronic hepatic failure. There was a close association between the onset and disappearance of clinical, electrophysiological and magnetic resonance imaging abnormalities.

  4. 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.

  5. Intraosseous mucoepidermoid carcinoma: a review of the diagnostic imaging features of four jaw cases.

    PubMed

    Chan, K C; Pharoah, M; Lee, L; Weinreb, I; Perez-Ordonez, B

    2013-01-01

    The purpose of this case series is to present the common features of intraosseous mucoepidermoid carcinoma (IMC) of the jaws in plain film and CT imaging. Two oral and maxillofacial radiologists reviewed and characterized the common features of four biopsy-proven cases of IMC in the jaws in plain film and CT imaging obtained from the files of the Department of Oral Radiology, Faculty of Dentistry, University of Toronto, Toronto, Canada. The common features are a well-defined sclerotic periphery, the presence of internal amorphous sclerotic bone and numerous small loculations, lack of septae bordering many of the loculations, and expansion and perforation of the outer cortical plate with extension into surrounding soft tissue. Other characteristics include tooth displacement and root resorption. The four cases of IMC reviewed have common imaging characteristics. All cases share some diagnostic imaging features with other multilocular-appearing entities of the jaws. However, the presence of amorphous sclerotic bone and malignant characteristics can be useful in the differential diagnosis.

  6. 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

  7. 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.

  8. Automatic identification of Caenorhabditis elegans in population images by shape energy features.

    PubMed

    Ochoa, D; Gautama, S; Philips, W

    2010-05-01

    Experiments on model organisms are used to extend the understanding of complex biological processes. In Caenorhabditis elegans studies, populations of specimens are sampled to measure certain morphological properties and a population is characterized based on statistics extracted from such samples. Automatic detection of C. elegans in such culture images is a difficult problem. The images are affected by clutter, overlap and image degradations. In this paper, we exploit shape and appearance differences between C. elegans and non-C. elegans segmentations. Shape information is captured by optimizing a parametric open contour model on training data. Features derived from the contour energies are proposed as shape descriptors and integrated in a probabilistic framework. These descriptors are evaluated for C. elegans detection in culture images. Our experiments show that measurements extracted from these samples correlate well with ground truth data. These positive results indicate that the proposed approach can be used for quantitative analysis of complex nematode images.

  9. Separation of malignant and benign masses using image and segmentation features

    NASA Astrophysics Data System (ADS)

    Kinnard, Lisa M.; Lo, Shih-Chung B.; Wang, Paul C.; Freedman, Matthew T.; Chouikha, Mohamed F.

    2003-05-01

    The purpose of this study is to investigate the efficacy of image features versus likelihood features of tumor boundaries for differentiating benign and malignant tumors and to compare the effectiveness of two neural networks in the classification study: (1) circular processing-based neural network and (2) conventional Multilayer Perceptron (MLP). The segmentation method used is an adaptive region growing technique coupled with a fuzzy shadow approach and maximum likelihood analyzer. Intensity, shape, texture, and likelihood features were calculated for the extracted Region of Interest (ROI). We performed these studies: experiment number 1 utilized image features used as inputs and the MLP for classification, experiment number 2 utilized image features used as inputs and the neural net with circular processing for classification, and experiment number 3 used likelihood values as inputs and the MLP for classification. The experiments were validated using an ROC methodology. We have tested these methods on 51 mammograms using a leave-one-case-out experiment (i.e., Jackknife procedure). The Az values for the four experiments were as follows: 0.66 in experiment number 1, 0.71 in experiment number 2, and 0.84 in experiment number 3.

  10. 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

  11. High-speed video imaging and digital analysis of microscopic features in contracting striated muscle cells

    NASA Astrophysics Data System (ADS)

    Roos, Kenneth P.; Taylor, Stuart R.

    1993-02-01

    The rapid motion of microscopic features such as the cross striations of single contracting muscle cells are difficult to capture with conventional optical microscopes, video systems, and image processing approaches. An integrated digital video imaging microscope system specifically designed to capture images from single contracting muscle cells at speeds of up to 240 Hz and to analyze images to extract features critical for the understanding of muscle contraction is described. This system consists of a brightfield microscope with immersion optics coupled to a high-speed charge-coupled device (CCD) video camera, super-VHS (S- VHS) and optical media disk video recording (OMDR) systems, and a semiautomated digital image analysis system. Components are modified to optimize spatial and temporal resolution to permit the evaluation of submicrometer features in real physiological time. This approach permits the critical evaluation of the magnitude, time course, and uniformity of contractile function throughout the volume of a single living cell with higher temporal and spatial resolutions than previously possible.

  12. Hepatic involvement in HELLP syndrome: an update with emphasis on imaging features.

    PubMed

    Perronne, Laetitia; Dohan, Anthony; Bazeries, Paul; Guerrache, Youcef; Fohlen, Audrey; Rousset, Pascal; Aubé, Christophe; Laurent, Valérie; Morel, Olivier; Boudiaf, Mourad; Hoeffel, Christine; Soyer, Philippe

    2015-10-01

    HELLP syndrome, which consists of hemolysis, elevated liver enzymes, and low platelet count is an unusual complication of pregnancy that is observed in only 10% to 15% of women with preeclampsia. Hepatic involvement in HELLP syndrome may present with various imaging features depending on the specific condition that includes nonspecific abnormalities such as perihepatic free fluid, hepatic steatosis, liver enlargement, and periportal halo that may precede more severe conditions such as hepatic hematoma and hepatic rupture with hemoperitoneum. Maternal clinical symptoms may be nonspecific and easily mistaken for a variety of other conditions that should be recognized. Because hepatic hematoma occurring in association with preeclampsia and HELLP syndrome is a potentially life-threatening complication, prompt depiction is critical and may help reduce morbidity and mortality. This review provides an update on demographics, risk factors, pathophysiology, and clinical features of hepatic complications due to HELLP syndrome along with a special emphasis on the imaging features of these uncommon conditions.

  13. Dynamic feature extraction of coronary artery motion using DSA image sequences.

    PubMed

    Puentes, J; Roux, C; Garreau, M; Coatrieux, J L

    1998-12-01

    This paper aims to define and describe features of the motion of coronary arteries in two and three dimensions, presented as geometrical parameters that identify motion patterns. The main left coronary artery centerlines, obtained from digital subtraction angiography (DSA) image sequences, are first reconstructed. Thereafter, global and local motion features are evaluated along the sequence. The global attributes are centerline and point trajectory lengths, displacement amplitude, and virtual reference point, while local attributes are displacement direction, perpendicular/radial components, rotation direction, and curvature and torsion. These kinetic features allow us to obtain a detailed quantitative description of the displacements of arteries' centerlines, as well as associated epicardium deformations. Our modeling of local attributes as quasi-homogeneous on a segment analysis, enables us to propose a novel numeric to symbolic image transformation, which provides the required facts for knowledge-based motion interpretation. Experimental results using real data are consistent with cardiac dynamic behavior.

  14. 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

  15. Feature discrimination and detection probability in synthetic aperture radar imaging system

    NASA Technical Reports Server (NTRS)

    Lipes, R. G.; Butman, S. A.

    1977-01-01

    Images obtained using synthetic aperture radar (SAR) systems can only represent the intensities of resolution cells in the scene of interest probabilistically since radar receiver noise and Rayleigh scattering of the transmitted radiation are always present. Consequently, when features to be identified differ only by their contribution to the mean power of the radar return, discrimination can be treated by detection theory. In this paper, we develop a 'sufficient statistic' for discriminating between competing features and compare it with some suboptimal methods frequently used. Discrimination is measured by probability of detection error and depends on number of samples or 'looks', signal-to-noise ratio (SNR), and ratio of mean power returns from the competing features. Our results show discrimination and image quality rapidly saturate with SNR (very small improvement for SNR not less than 10 dB) but continue to improve with increasing number of looks.

  16. 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.

  17. Application of fuzzy logic to feature extraction from images of agricultural material

    NASA Astrophysics Data System (ADS)

    Thompson, Bruce T.

    1999-11-01

    Imaging technology has extended itself from performing gauging on machined parts, to verifying labeling on consumer products, to quality inspection of a variety of man-made and natural materials. Much of this has been made possible by faster computers and algorithms used to extract useful information from the image. In the application of agricultural material, specifically tobacco leaves, the tremendous amount of natural variability in color and texture creates new challenges to image feature extraction. As with many imaging applications, the problem can be expressed as `I see it in the image, how can I get the computer to recognize it?' In this application, the goal is to measure the amount of thick stem pieces in an image of tobacco leaves. By backlighting the leaf, the stems appear dark on a lighter background. The difference in lightness of leaf versus darkness of stem is dependent on the orientation of the leaf and the amount of folding. Because of this, any image thresholding approach must be adaptive. Another factor that allows us to identify the stem from the leaf is shape. The stem is long and narrow, while dark folded leaf is larger and more oblate. These criteria under the image collection limitations create a good application for fuzzy logic. Several generalized classification algorithms, such as fuzzy c-means and fuzzy learning vector quantization, are evaluated and compared. In addition, fuzzy thresholding based on image shape and compactness are applied to this application.

  18. 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.

  19. 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

  20. Digital image forgery detection using JPEG features and local noise discrepancies.

    PubMed

    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.

  1. 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

  2. Classification of brain disease in magnetic resonance images using two-stage local feature fusion

    PubMed Central

    Li, Tao; Li, Wu; Yang, Yehui

    2017-01-01

    Background Many classification methods have been proposed based on magnetic resonance images. Most methods rely on measures such as volume, the cerebral cortical thickness and grey matter density. These measures are susceptible to the performance of registration and limited in representation of anatomical structure. This paper proposes a two-stage local feature fusion method, in which deformable registration is not desired and anatomical information is represented from moderate scale. Methods Keypoints are firstly extracted from scale-space to represent anatomical structure. Then, two kinds of local features are calculated around the keypoints, one for correspondence and the other for representation. Scores are assigned for keypoints to quantify their effect in classification. The sum of scores for all effective keypoints is used to determine which group the test subject belongs to. Results We apply this method to magnetic resonance images of Alzheimer's disease and Parkinson's disease. The advantage of local feature in correspondence and representation contributes to the final classification. With the help of local feature (Scale Invariant Feature Transform, SIFT) in correspondence, the performance becomes better. Local feature (Histogram of Oriented Gradient, HOG) extracted from 16×16 cell block obtains better results compared with 4×4 and 8×8 cell block. Discussion This paper presents a method which combines the effect of SIFT descriptor in correspondence and the representation ability of HOG descriptor in anatomical structure. This method has the potential in distinguishing patients with brain disease from controls. PMID:28207873

  3. 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.

  4. 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.

  5. 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 %.

  6. Semi-Automatic Normalization of Multitemporal Remote Images Based on Vegetative Pseudo-Invariant Features

    PubMed Central

    Garcia-Torres, Luis; Caballero-Novella, Juan J.; Gómez-Candón, David; De-Castro, Ana Isabel

    2014-01-01

    A procedure to achieve the semi-automatic relative image normalization of multitemporal remote images of an agricultural scene called ARIN was developed using the following procedures: 1) defining the same parcel of selected vegetative pseudo-invariant features (VPIFs) in each multitemporal image; 2) extracting data concerning the VPIF spectral bands from each image; 3) calculating the correction factors (CFs) for each image band to fit each image band to the average value of the image series; and 4) obtaining the normalized images by linear transformation of each original image band through the corresponding CF. ARIN software was developed to semi-automatically perform the ARIN procedure. We have validated ARIN using seven GeoEye-1 satellite images taken over the same location in Southern Spain from early April to October 2010 at an interval of approximately 3 to 4 weeks. The following three VPIFs were chosen: citrus orchards (CIT), olive orchards (OLI) and poplar groves (POP). In the ARIN-normalized images, the range, standard deviation (s. d.) and root mean square error (RMSE) of the spectral bands and vegetation indices were considerably reduced compared to the original images, regardless of the VPIF or the combination of VPIFs selected for normalization, which demonstrates the method’s efficacy. The correlation coefficients between the CFs among VPIFs for any spectral band (and all bands overall) were calculated to be at least 0.85 and were significant at P = 0.95, indicating that the normalization procedure was comparably performed regardless of the VPIF chosen. ARIN method was designed only for agricultural and forestry landscapes where VPIFs can be identified. PMID:24604031

  7. Investigation of automated feature extraction techniques for applications in cancer detection from multispectral histopathology images

    NASA Astrophysics Data System (ADS)

    Harvey, Neal R.; Levenson, Richard M.; Rimm, David L.

    2003-05-01

    Recent developments in imaging technology mean that it is now possible to obtain high-resolution histological image data at multiple wavelengths. This allows pathologists to image specimens over a full spectrum, thereby revealing (often subtle) distinctions between different types of tissue. With this type of data, the spectral content of the specimens, combined with quantitative spatial feature characterization may make it possible not only to identify the presence of an abnormality, but also to classify it accurately. However, such are the quantities and complexities of these data, that without new automated techniques to assist in the data analysis, the information contained in the data will remain inaccessible to those who need it. We investigate the application of a recently developed system for the automated analysis of multi-/hyper-spectral satellite image data to the problem of cancer detection from multispectral histopathology image data. The system provides a means for a human expert to provide training data simply by highlighting regions in an image using a computer mouse. Application of these feature extraction techniques to examples of both training and out-of-training-sample data demonstrate that these, as yet unoptimized, techniques already show promise in the discrimination between benign and malignant cells from a variety of samples.

  8. Fast localization of the optic disc using projection of image features.

    PubMed

    Mahfouz, Ahmed E; Fahmy, Ahmed S

    2010-12-01

    Optic Disc (OD) localization is an important pre-processing step that significantly simplifies subsequent segmentation of the OD and other retinal structures. Current OD localization techniques suffer from impractically-high computation times (few minutes per image). In this work, we present a fast technique that requires less than a second to localize the OD. The technique is based upon obtaining two projections of certain image features that encode the x- and y- coordinates of the OD. The resulting 1-D projections are then searched to determine the location of the OD. This avoids searching the 2-D image space and, thus, enhances the speed of the OD localization process. Image features such as retinal vessels orientation and the OD brightness are used in the current method. Four publicly-available databases, including STARE and DRIVE, are used to evaluate the proposed technique. The OD was successfully located in 330 images out of 340 images (97%) with an average computation time of 0.65 s.

  9. 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.

  10. Recent development of feature extraction and classification multispectral/hyperspectral images: a systematic literature review

    NASA Astrophysics Data System (ADS)

    Setiyoko, A.; Dharma, I. G. W. S.; Haryanto, T.

    2017-01-01

    Multispectral data and hyperspectral data acquired from satellite sensor have the ability in detecting various objects on the earth ranging from low scale to high scale modeling. These data are increasingly being used to produce geospatial information for rapid analysis by running feature extraction or classification process. Applying the most suited model for this data mining is still challenging because there are issues regarding accuracy and computational cost. This research aim is to develop a better understanding regarding object feature extraction and classification applied for satellite image by systematically reviewing related recent research projects. A method used in this research is based on PRISMA statement. After deriving important points from trusted sources, pixel based and texture-based feature extraction techniques are promising technique to be analyzed more in recent development of feature extraction and classification.

  11. Finger-vein image recognition combining modified Hausdorff distance with minutiae feature matching.

    PubMed

    Yu, Cheng-Bo; Qin, Hua-Feng; Cui, Yan-Zhe; Hu, Xiao-Qian

    2009-12-01

    In this paper, we propose a novel method for finger-vein recognition. We extract the features of the vein patterns for recognition. Then, the minutiae features included bifurcation points and ending points are extracted from these vein patterns. These feature points are used as a geometric representation of the vein patterns shape. Finally, the modified Hausdorff distance algorithm is provided to evaluate the identification ability among all possible relative positions of the vein patterns shape. This algorithm has been widely used for comparing point sets or edge maps since it does not require point correspondence. Experimental results show that these minutiae feature points can be used to perform personal verification tasks as a geometric representation of the vein patterns shape. Furthermore, by this developed method, we can achieve robust image matching under different lighting conditions.

  12. 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

  13. 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.

  14. Investigation of image corner features matching algorithm based on heuristic local geometric constrained strategy

    NASA Astrophysics Data System (ADS)

    An, Ru; Wang, Huilin; Fen, Xuezhi; Xu, Daxin; Ruan, Renzong

    2005-11-01

    The main aim of the study is to improve the performance of image matching algorithm of Scene Matching Aided Navigation System. In the paper, corner-based image matching algorithm with automatic search of homonymous corner pairs is discussed. Gaussian Low-pass Filter with different kernels according to the spatial resolution of reference image and real-time image are applied to the image in preprocessing stage to remove noise, to get over spatial resolution difference between reference image and real-time image and to enhance the repeatability of corner detection. A novel fast corner detector, which is based on SUSAN and the geometric structure analysis, is designed to extract corner features. Normalized co-correlation algorithm is applied in search of homonymous corner pairs through a small window centering corners. A heuristic local geometrically constrained strategy is employed to remove mis-matched corner pairs in initial matching stage. In the end, matched corners, in combination with a suitable polynomial algorithm, are used to match and rectify images.

  15. Co-occurrence texture feature variation for a moving window over apple images

    NASA Astrophysics Data System (ADS)

    Throop, James A.; Aneshansley, Daniel J.; Upchurch, Bruce L.

    1995-01-01

    Near infrared reflectance (NIR) images of bruised `Delicious' applies were converted to images of texture properties. Bruises of two sizes (11 mm and 26 mm diameter) and two ages (1 d and 90 d) were examined. Seven texture properties (variance, entropy, product moment, difference entropy, inverse difference, difference variance, and sum variance) were computed from a cooccurrence matrix. Window size and neighborhood distance for the cooccurrence matrix were set to optimize the texture contrast between bruised and unbruised tissue. The window position was incrementally scanned over the entire apple image creating a new image of texture values. Four neighborhood directions (0 degree(s), 90 degree(s), 45 degree(s), 135 degree(s)) were considered. Sum variance was the only texture property that showed improved contrast of the bruised/unbruised areas relative to the original NIR image. All other texture properties produced images that highlighted the edge of the bruise. The variance property produced images with the best defined bruise edges irregardless of bruise size or age. Variance and sum variance show promise as additional features to the grey tone image for discriminating apple bruises.

  16. Possibility Study of Scale Invariant Feature Transform (SIFT) Algorithm Application to Spine Magnetic Resonance Imaging.

    PubMed

    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.

  17. Automatic tracking of linear features on SPOT images using dynamic programming

    NASA Astrophysics Data System (ADS)

    Bonnefon, Regis; Dherete, Pierre; Desachy, Jacky

    1999-12-01

    Detection of geographic elements on images is important in the perspective of adding new elements in geographic databases which are sometimes old and so, some elements are not represented. Our goal is to look for linear features like roads, rivers or railways on SPOT images with a resolution of 10 meters. Several methods allow this detection to be realized and may be classified in three categories: (1) Detection operators: the best known is the DUDA Road Operator which determine the belonging degree of a pixel to a linear feature from several 5 X 5 filters. Results are often unsatisfactory. It exists too the Infinite Size Exponential Filter (ISEF), which is a derivative filter and allows edge, valley or roof profile to be found on the image. It can be utilized as an additional information for others methods. (2) Structural tracking: from a starting point, an analysis in several directions is performed to determine the best next point (features may be: homogeneity of radiometry, contrast with environment, ...). From this new point and with an updated direction, the process goes on. Difficulty of these methods is the consideration of occlusions (bridges, tunnels, dense vegetation, ...). (3) Dynamic programming: F* algorithm and snakes are the best known. They allow a path with a minimal cost to be found in a search window. Occlusions are not a problem but two points or more near the searched linear feature must be known to define the window. The method described below is a mixture of structural tracking and dynamic programming (F* algorithm).

  18. Feature extraction from 3D lidar point clouds using image processing methods

    NASA Astrophysics Data System (ADS)

    Zhu, Ling; Shortridge, Ashton; Lusch, David; Shi, Ruoming

    2011-10-01

    Airborne LiDAR data have become cost-effective to produce at local and regional scales across the United States and internationally. These data are typically collected and processed into surface data products by contractors for state and local communities. Current algorithms for advanced processing of LiDAR point cloud data are normally implemented in specialized, expensive software that is not available for many users, and these users are therefore unable to experiment with the LiDAR point cloud data directly for extracting desired feature classes. The objective of this research is to identify and assess automated, readily implementable GIS procedures to extract features like buildings, vegetated areas, parking lots and roads from LiDAR data using standard image processing tools, as such tools are relatively mature with many effective classification methods. The final procedure adopted employs four distinct stages. First, interpolation is used to transfer the 3D points to a high-resolution raster. Raster grids of both height and intensity are generated. Second, multiple raster maps - a normalized surface model (nDSM), difference of returns, slope, and the LiDAR intensity map - are conflated to generate a multi-channel image. Third, a feature space of this image is created. Finally, supervised classification on the feature space is implemented. The approach is demonstrated in both a conceptual model and on a complex real-world case study, and its strengths and limitations are addressed.

  19. 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.

  20. Multi-Center Prediction of Hemorrhagic Transformation in Acute Ischemic Stroke using Permeability Imaging Features

    PubMed Central

    Scalzo, Fabien; Alger, Jeffry R.; Hu, Xiao; Saver, Jeffrey L.; Dani, Krishna A.; Muir, Keith W.; Demchuk, Andrew M.; Coutts, Shelagh B.; Luby, Marie; Warach, Steven; Liebeskind, David S.

    2013-01-01

    Permeability images derived from magnetic resonance (MR) perfusion images are sensitive to blood-brain barrier derangement of the brain tissue and have been shown to correlate with subsequent development of hemorrhagic transformation (HT) in acute ischemic stroke. This paper presents a multi-center retrospective study that evaluates the predictive power in terms of HT of six permeability MRI measures including contrast slope (CS), final contrast (FC), maximum peak bolus concentration (MPB), peak bolus area (PB), relative recirculation (rR), and percentage recovery (%R). Dynamic T2*-weighted perfusion MR images were collected from 263 acute ischemic stroke patients from four medical centers. An essential aspect of this study is to exploit a classifier-based framework to automatically identify predictive patterns in the overall intensity distribution of the permeability maps. The model is based on normalized intensity histograms that are used as input features to the predictive model. Linear and nonlinear predictive models are evaluated using a crossvalidation to measure generalization power on new patients and a comparative analysis is provided for the different types of parameters. Results demonstrate that perfusion imaging in acute ischemic stroke can predict HT with an average accuracy of more than 85% using a predictive model based on a nonlinear regression model. Results also indicate that the permeability feature based on the percentage of recovery performs significantly better than the other features. This novel model may be used to refine treatment decisions in acute stroke. PMID:23587928

  1. 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.

  2. 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.

  3. Improving scale invariant feature transform with local color contrastive descriptor for image classification

    NASA Astrophysics Data System (ADS)

    Guo, Sheng; Huang, Weilin; Qiao, Yu

    2017-01-01

    Image representation and classification are two fundamental tasks toward version understanding. Shape and texture provide two key features for visual representation and have been widely exploited in a number of successful local descriptors, e.g., scale invariant feature transform (SIFT), local binary pattern descriptor, and histogram of oriented gradient. Unlike these gradient-based descriptors, this paper presents a simple yet efficient local descriptor, named local color contrastive descriptor (LCCD), which captures the contrastive aspects among local regions or color channels for image representation. LCCD is partly inspired by the neural science facts that color contrast plays important roles in visual perception and there exist strong linkages between color and shape. We leverage f-divergence as a robust measure to estimate the contrastive features between different spatial locations and multiple channels. Our descriptor enriches local image representation with both color and contrast information. Due to that LCCD does not explore any gradient information, individual LCCD does not yield strong performance. But we verified experimentally that LCCD can compensate strongly SIFT. Extensive experimental results on image classification show that our descriptor improves the performance of SIFT substantially by combination on three challenging benchmarks, including MIT Indoor-67 database, SUN397, and PASCAL VOC 2007.

  4. Can digital image forgery detection unevadable? A case study: color filter array interpolation statistical feature recovery

    NASA Astrophysics Data System (ADS)

    Huang, Yizhen

    2005-07-01

    Digital image forgery detection is becoming increasing important. In recently 2 years, a new upsurge has been started to study direct detection methods, which utilize the hardware features of digital cameras. Such features may be weakened or lost once tampered, or they may not be consistent if synthesizing several images into a single one. This manuscript first clarifies the concept of trueness of digital images and summarizes these methods with their crack by a general model. The recently proposed EM algorithm plus Fourier transform that checks the Color Filter Array (CFA) interpolation statistical feature (ISF) is taken as a case study. We propose 3 methods to recover the CFA-ISF of a fake image: (1) artificial CFA interpolation (2) a linear CFA-ISF recovery model with optimal uniform measure (3) a quadratic CFA-ISF recovery model with least square measure. A software prototype CFA-ISF Indicator & Adjustor integrating the detection and anti-detection algorithms is developed and shown. Experiments under our product validate the effectiveness of our methods.

  5. 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.

  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. Joints and their relations as critical features in action discrimination: evidence from a classification image method.

    PubMed

    van Boxtel, Jeroen J A; Lu, Hongjing

    2015-01-20

    Classifying an action as a runner or a walker is a seemingly effortless process. However, it is difficult to determine which features are used with hypothesis-driven research, because biological motion stimuli generally consist of about a dozen joints, yielding an enormous number of potential relationships among them. Here, we develop a hypothesis-free approach based on a classification image method, using experimental data from relatively few trials (∼1,000 trials per subject). Employing ambiguous actions morphed between a walker and a runner, we identified three types of features that play important roles in discriminating bipedal locomotion presented in a side view: (a) critical joint feature, supported by the finding that the similarity of the movements of feet and wrists to prototypical movements of these joints were most reliably used across all participants; (b) structural features, indicated by contributions from almost all other joints, potentially through a form-based analysis; and (c) relational features, revealed by statistical correlations between joint contributions, specifically relations between the two feet, and relations between the wrists/elbow and the hips. When the actions were inverted, only critical joint features remained to significantly influence discrimination responses. When actions were presented with continuous depth rotation, critical joint features and relational features associated strongly with responses. Using a double-pass paradigm, we estimated that the internal noise is about twice as large as the external noise, consistent with previous findings. Overall, our novel design revealed a rich set of critical features that are used in action discrimination. The visual system flexibly selects a subset of features depending on viewing conditions.

  8. Plant species classification using flower images-A comparative study of local feature representations.

    PubMed

    Seeland, Marco; Rzanny, Michael; Alaqraa, Nedal; Wäldchen, Jana; Mäder, Patrick

    2017-01-01

    Steady improvements of image description methods induced a growing interest in image-based plant species classification, a task vital to the study of biodiversity and ecological sensitivity. Various techniques have been proposed for general object classification over the past years and several of them have already been studied for plant species classification. However, results of these studies are selective in the evaluated steps of a classification pipeline, in the utilized datasets for evaluation, and in the compared baseline methods. No study is available that evaluates the main competing methods for building an image representation on the same datasets allowing for generalized findings regarding flower-based plant species classification. The aim of this paper is to comparatively evaluate methods, method combinations, and their parameters towards classification accuracy. The investigated methods span from detection, extraction, fusion, pooling, to encoding of local features for quantifying shape and color information of flower images. We selected the flower image datasets Oxford Flower 17 and Oxford Flower 102 as well as our own Jena Flower 30 dataset for our experiments. Findings show large differences among the various studied techniques and that their wisely chosen orchestration allows for high accuracies in species classification. We further found that true local feature detectors in combination with advanced encoding methods yield higher classification results at lower computational costs compared to commonly used dense sampling and spatial pooling methods. Color was found to be an indispensable feature for high classification results, especially while preserving spatial correspondence to gray-level features. In result, our study provides a comprehensive overview of competing techniques and the implications of their main parameters for flower-based plant species classification.

  9. 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

  10. 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.

  11. Do A-type stars flare?

    NASA Astrophysics Data System (ADS)

    Pedersen, M. G.; Antoci, V.; Korhonen, H.; White, T. R.; Jessen-Hansen, J.; Lehtinen, J.; Nikbakhsh, S.; Viuho, J.

    2017-04-01

    For flares to be generated, stars have to have a sufficiently deep outer convection zone (F5 and later), strong large-scale magnetic fields (Ap/Bp-type stars) or strong, radiatively driven winds (B5 and earlier). Normal A-type stars possess none of these and therefore should not flare. Nevertheless, flares have previously been detected in the Kepler light curves of 33 A-type stars and interpreted to be intrinsic to the stars. Here, we present new and detailed analyses of these 33 stars, imposing very strict criteria for the flare detection. We confirm the presence of flare-like features in 27 of the 33 A-type stars. A study of the pixel data and the surrounding field of view reveals that 14 of these 27 flaring objects have overlapping neighbouring stars and five stars show clear contamination in the pixel data. We have obtained high-resolution spectra for 2/3 of the entire sample and confirm that our targets are indeed A-type stars. Detailed analyses revealed that 11 out of 19 stars with multiple epochs of observations are spectroscopic binaries. Furthermore, and contrary to previous studies, we find that the flares can originate from a cooler, unresolved companion. We note the presence of Hα emission in eight stars. Whether this emission is circumstellar or magnetic in origin is unknown. In summary, we find possible alternative explanations for the observed flares for at least 19 of the 33 A-type stars, but find no truly convincing target to support the hypothesis of flaring A-type stars.

  12. Nonparametric feature extraction for classification of hyperspectral images with limited training samples

    NASA Astrophysics Data Syst