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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

    2017-09-01

    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. © 2017 Wiley Periodicals, Inc.

  2. Whole slide image with image analysis of atypical bile duct brushing: Quantitative features predictive of malignancy.

    PubMed

    Collins, Brian T; Weimholt, R Cody

    2015-01-01

    Whole slide images (WSIs) involve digitally capturing glass slides for microscopic computer-based viewing and these are amenable to quantitative image analysis. Bile duct (BD) brushing can show morphologic features that are categorized as indeterminate for malignancy. The study aims to evaluate quantitative morphologic features of atypical categories of BD brushing by WSI analysis for the identification of criteria predictive of malignancy. Over a 3-year period, BD brush specimens with indeterminate diagnostic categorization (atypical to suspicious) were subjected to WSI analysis. Ten well-visualized groups with morphologic atypical features were selected per case and had the quantitative analysis performed for group area, individual nuclear area, the number of nuclei per group, N: C ratio and nuclear size differential. There were 28 cases identified with 17 atypical and 11 suspicious. The average nuclear area was 63.7 µm(2) for atypical and 80.1 µm(2) for suspicious (+difference 16.4 µm(2); P = 0.002). The nuclear size differential was 69.7 µm(2) for atypical and 88.4 µm(2) for suspicious (+difference 18.8 µm(2); P = 0.009). An average nuclear area >70 µm(2) had a 3.2 risk ratio for suspicious categorization. The quantitative criteria findings as measured by image analysis on WSI showed that cases categorized as suspicious had more nuclear size pleomorphism (+18.8 µm(2)) and larger nuclei (+16.4 µm(2)) than those categorized as atypical. WSI with morphologic image analysis can demonstrate quantitative statistically significant differences between atypical and suspicious BD brushings and provide objective criteria that support the diagnosis of carcinoma.

  3. 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

  4. Comparative Analysis of the Magnetic Resonance Imaging Features Between Anaplastic Meningioma and Atypical Meningioma.

    PubMed

    Liu, Hong; Zhou, Junlin; Li, Wenyi; Liu, Guangyao

    2016-05-01

    The aim of the study was to investigate the differences in the imaging feature between anaplastic meningioma (World Health Organization grade III) and atypical meningioma (World Health Organization grade II), summarize its specificity of image features, and provide the basis for accurate preoperative diagnosis. The magnetic resonance imaging features of 20 patients of anaplastic meningioma were compared with those of 30 patients of atypical meningioma retrospectively, all of which were confirmed by surgery and pathology. The imaging features of the 2 groups of tumors were statistically analyzed using χ tests. The 2 tumor types differed in several features, including lobulated or irregular shape (P < 0.05), cystic and necrotic changes (P < 0.05), peritumoral edema (P < 0.05), and brain-tumor interface (P < 0.01). There were no significant differences in hemorrhage, homogeneous enhancement of the tumor, dural tail sign, or adjacent bone change (P > 0.05). Differences in the imaging feature between anaplastic meningioma and atypical meningioma can improve the differential diagnosis and allow a more appropriate approach to therapy. The article focuses on examining the differences of the magnetic resonance imaging features between anaplastic meningiomas and atypical meningiomas have been examined in only a few studies.

  5. 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

  6. Atypical imaging features of tuberculous spondylitis: case report with literature review.

    PubMed

    Momjian, Rita; George, Mina

    2014-11-01

    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.

  7. [Cryptogenic organizing pneumonia: typical and atypical imaging features on computed tomography].

    PubMed

    Hamer, O W; Silva, C I; Müller, N L

    2008-07-01

    Organizing pneumonia (OP) occurs without any identifiable cause ("cryptogenic organizing pneumonia") as well as secondary to a multitude of disorders of various origins ("secondary organizing pneumonia"). Possible triggers are infections, drugs, collagen vascular disease, inflammatory bowel disease, transplantations, and radiation directed to the chest. The present manuscript provides an overview of the histopathological, clinical and CT imaging features of OP. Classic CT morphologies (peripheral and peribronchovascular consolidations and ground glass opacities) and atypical imaging features (nodules, crazy paving, lines and bands, perilobular consolidations and the reversed halo sign) are discussed.

  8. Atypical imaging features of primary central nervous system lymphoma that mimics glioblastoma: utility of intravoxel incoherent motion MR imaging.

    PubMed

    Suh, Chong Hyun; Kim, Ho Sung; Lee, Seung Soo; Kim, Namkug; Yoon, Hee Mang; Choi, Choong-Gon; Kim, Sang Joon

    2014-08-01

    To determine the utility of intravoxel incoherent motion (IVIM)-derived perfusion and diffusion parameters for differentiation of atypical primary central nervous system lymphoma (PCNSL) from glioblastoma in patients who do not have acquired immunodeficiency syndrome. The institutional review board approved this retrospective study and waived the informed consent requirement. Sixty patients with either pathologic analysis-confirmed atypical PCNSLs (n = 19) or glioblastomas (n = 41) were assessed by using maximum IVIM-derived perfusion fraction (f) and minimum true IVIM diffusion parameter (D). Two readers independently calculated IVIM parameters and maximum normalized cerebral blood volume (nCBV) and minimum apparent diffusion coefficient. Leave-one-out cross-validation and intraclass correlation coefficients were assessed to determine reliability and reproducibility of the parameters, respectively. Mean maximum f was significantly higher in the glioblastoma group than in the atypical PCNSL group (reader 1, 0.101 ± 0.016 [standard deviation] vs 0.021 ± 0.010; P < .001; reader 2: 0.107 ± 0.024 vs 0.027 ± 0.015; P < .001). Mean minimum D did not significantly differ between the two groups (reader 1, P = .202; reader 2, P = .091). By using maximum f as a discriminative index, respective sensitivity and specificity were 89.5% and 95.1% for reader 1 and 84.2% and 95.1% for reader 2. There was a significant positive correlation between maximum f and the corresponding nCBV (r = 0.68; P < .001). The intraclass correlation coefficient between readers was highest for measurement of maximum f (intraclass correlation coefficient, 0.92). IVIM imaging can be used as a noninvasive imaging method to differentiate malignant brain tumors that show similar conventional MR imaging features.

  9. Atypical MRI features in soft-tissue arteriovenous malformation: a novel imaging appearance with radiologic-pathologic correlation.

    PubMed

    Patel, Anand S; Schulman, Joshua M; Ruben, Beth S; Hoffman, William Y; Dowd, Christopher F; Frieden, Ilona J; Hess, Christopher P

    2015-09-01

    The absence of a discrete mass, surrounding signal abnormality and solid enhancement are imaging features that have traditionally been used to differentiate soft-tissue arteriovenous malformations from vascular tumors on MRI. We have observed that these findings are not uncommon in arteriovenous malformations, which may lead to misdiagnosis or inappropriate treatment. To estimate the frequency of atypical MRI features in soft-tissue arteriovenous malformations and assess their relationship to lesion size, location, tissue type involved and vascular architecture. Medical records, MRI and histopathology were reviewed in consecutive patients with soft-tissue arteriovenous malformations in a multidisciplinary vascular anomalies clinic. Arteriovenous malformations were divided into those with and without atypical MRI findings (perilesional T2 signal abnormality, enhancement and/or a soft-tissue mass). Lesion location, size, tissue involved and vascular architecture were also compared between groups. Tissue stains were reviewed in available biopsy or resection specimens to assess relationships between MRI findings and histopathology. Thirty patients with treatment-naïve arteriovenous malformations were included. Fifteen lesions demonstrated atypical MRI. There was no difference in age, gender, lesion size or involved body part between the groups. However, more than half of the atypical lesions demonstrated multicompartmental involvement, and tiny intralesional flow voids were more common in atypical arteriovenous malformations. Histopathology also differed in atypical cases, showing densely packed endothelial cells with connective tissue architectural distortion and edema. Arteriovenous malformations may exhibit features of a vascular tumor on MRI, particularly when multicompartmental and/or containing tiny internal vessels. These features are important to consider in suspected fast-flow vascular malformations and may have implications with respect to their treatment.

  10. Uremic encephalopathy with atypical magnetic resonance features on diffusion-weighted images.

    PubMed

    Kang, Eugene; Jeon, Se Jeong; Choi, See-Sung

    2012-01-01

    Uremic encephalopathy is a well-known disease with typical MR findings including bilateral vasogenic or cytotoxic edema at the cerebral cortex or basal ganglia. Involvement of the basal ganglia has been very rarely reported, typically occurring in uremic-diabetic patients. We recently treated a patient who had non-diabetic uremic encephalopathy with an atypical lesion distribution involving the supratentorial white matter, without cortical or basal ganglia involvement. To the best of our knowledge, this is only the second reported case of non-diabetic uremic encephalopathy with atypical MR findings.

  11. Uremic Encephalopathy with Atypical Magnetic Resonance Features on Diffusion-Weighted Images

    PubMed Central

    Kang, Eugene; Choi, See-Sung

    2012-01-01

    Uremic encephalopathy is a well-known disease with typical MR findings including bilateral vasogenic or cytotoxic edema at the cerebral cortex or basal ganglia. Involvement of the basal ganglia has been very rarely reported, typically occurring in uremic-diabetic patients. We recently treated a patient who had non-diabetic uremic encephalopathy with an atypical lesion distribution involving the supratentorial white matter, without cortical or basal ganglia involvement. To the best of our knowledge, this is only the second reported case of non-diabetic uremic encephalopathy with atypical MR findings. PMID:23118581

  12. Uremic parkinsonism with atypical phenotypes and radiologic features.

    PubMed

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

    2016-04-01

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

  13. Breast metastases from extramammary malignancies: typical and atypical ultrasound features.

    PubMed

    Mun, Sung Hee; Ko, Eun Young; Han, Boo-Kyung; Shin, Jung Hee; Kim, Suk Jung; Cho, Eun Yoon

    2014-01-01

    Breast metastases from extramammary malignancies are uncommon. The most common sources are lymphomas/leukemias and melanomas. Some of the less common sources include carcinomas of the lung, ovary, and stomach, and infrequently, carcinoid tumors, hypernephromas, carcinomas of the liver, tonsil, pleura, pancreas, cervix, perineum, endometrium and bladder. Breast metastases from extramammary malignancies have both hematogenous and lymphatic routes. According to their routes, there are common radiological features of metastatic diseases of the breast, but the features are not specific for metastases. Typical ultrasound (US) features of hematogenous metastases include single or multiple, round to oval shaped, well-circumscribed hypoechoic masses without spiculations, calcifications, or architectural distortion; these masses are commonly located superficially in subcutaneous tissue or immediately adjacent to the breast parenchyma that is relatively rich in blood supply. Typical US features of lymphatic breast metastases include diffusely and heterogeneously increased echogenicities in subcutaneous fat and glandular tissue and a thick trabecular pattern with secondary skin thickening, lymphedema, and lymph node enlargement. However, lesions show variable US features in some cases, and differentiation of these lesions from primary breast cancer or from benign lesions is difficult. In this review, we demonstrate various US appearances of breast metastases from extramammary malignancies as typical and atypical features, based on the results of US and other imaging studies performed at our institution. Awareness of the typical and atypical imaging features of these lesions may be helpful to diagnose metastatic lesions of the breast.

  14. 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.

  15. Imaging the neurobiological substrate of atypical depression by SPECT.

    PubMed

    Pagani, Marco; Salmaso, Dario; Nardo, Davide; Jonsson, Cathrine; Jacobsson, Hans; Larsson, Stig A; Gardner, Ann

    2007-01-01

    Neurobiological abnormalities underlying atypical depression have previously been suggested. The purpose of this study was to explore differences at functional brain imaging between depressed patients with and without atypical features and healthy controls. Twenty-three out-patients with chronic depressive disorder recruited from a service for patients with audiological symptoms were investigated. Eleven fulfilled the DSM-IV criteria for atypical depression (mood reactivity and at least two of the following: weight gain, hypersomnia, leaden paralysis and interpersonal rejection sensitivity). Twenty-three healthy subjects served as controls. Voxel-based analysis was applied to explore differences in (99m)Tc-HMPAO uptake between groups. Patients in the atypical group had a higher prevalence of bilateral hearing impairment and higher depression and somatic distress ratings at the time of SPECT. Significantly higher tracer uptake was found bilaterally in the atypical group as compared with the non-atypicals in the sensorimotor (Brodmann areas, BA1-3) and premotor cortex in the superior frontal gyri (BA6), in the middle frontal cortex (BA8), in the parietal associative cortex (BA5, BA7) and in the inferior parietal lobule (BA40). Significantly lower tracer distribution was found in the right hemisphere in the non-atypicals compared with the controls in BA6, BA8, BA44, BA45 and BA46 in the frontal cortex, in the orbito-frontal cortex (BA11, BA47), in the postcentral parietal cortex (BA2) and in the multimodal association parietal cortex (BA40). The differences found between atypical and non-atypical depressed patients suggest different neurobiological substrates in these patient groups. The putative links with the clinical features of atypical depression are discussed. These findings encourage the use of functional neuroimaging in psychiatric disorders.

  16. 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.

  17. 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.

  18. 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

  19. Atypical features in depression: Association with obesity and bipolar disorder.

    PubMed

    Łojko, Dorota; Buzuk, Grzegorz; Owecki, Maciej; Ruchała, Marek; Rybakowski, Janusz K

    2015-10-01

    Depression with atypical features amounts to a significant proportion of depressed patients. Studies have shown its association with bipolarity and, recently, with obesity. In this study, we investigated atypical features of depression in relation to overweight/obesity in three diagnostic categories: unipolar depression, bipolar depression and dysthymia. Out of 512 depressed patients screened, we recruited 182 research subjects, consisting of 91 pairs, matched by age, gender and diagnosis, in which one member of the pair was within the normal weight range (BMI≤25) and the other was either overweight or obese (BMI>25). There were 35 pairs with unipolar depression, 27 with bipolar depression and 29 with dysthymia. Symptoms of atypical depression, such as increased appetite, hypersomnia, leaden paralysis, longstanding pattern of interpersonal rejection sensitivity, and, a significant weight gain in the past 3 months, were assessed. All the symptoms of atypical depression were significantly more pronounced in those depressed patients with a BMI>25, compared with depressed subjects with a normal weight. Except for hypersomnia, these symptoms scored significantly higher in women compared to men. Among the diagnostic categories, symptoms of atypical depression were significantly higher in patients with bipolar disorder compared with both major depressive disorder and dysthymia. The preponderance of women, the assessment of atypical depression by adaptation of the DSM criteria, entirely Polish population, specificity of selection criteria. The results demonstrated a higher intensity of atypical depression's symptoms in overweight/obese depressed patients. They also confirm the association between obesity and bipolarity. Copyright © 2015 Elsevier B.V. All rights reserved.

  20. 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.

  1. Amyloid imaging in dementias with atypical presentation.

    PubMed

    Wolk, David A; Price, Julie C; Madeira, Charles; Saxton, Judy A; Snitz, Beth E; Lopez, Oscar L; Mathis, Chester A; Klunk, William E; DeKosky, Steven T

    2012-09-01

    With the potential emergence of disease specific therapies, an accurate biomarker of Alzheimer's Disease pathology is needed in cases in which the underlying etiology is uncertain. We explored the potential value of amyloid imaging in patients with atypical presentations of dementia. Twenty-eight patients with atypical dementia underwent positron emission tomography imaging with the amyloid imaging tracer Pittsburgh compound B (PiB). Twenty-six had [18F]fluoro-2-deoxy-D-glucose positron emission tomography scans. After extensive clinical evaluation, this group of patients generated considerable diagnostic uncertainty and received working diagnoses that included possible Alzheimer's disease (AD), focal dementias (e.g., posterior cortical atrophy [PCA]), or cases in which no clear diagnostic category could be determined (dementia of uncertain etiology). Patients were classified as PiB-positive, PiB-negative, or PiB-intermediate, based on objective criteria. Anterior-posterior and left-right indices of PiB and [18F]fluoro-2-deoxy-D-glucose uptake were calculated to examine differences in distribution of amyloid pathology and metabolic changes associated with clinical phenotype. Eleven patients (39%) were PiB positive, 16 were PiB negative (57%), and one (4%) was PiB intermediate. By diagnostic category, three of 10 patients (30%) with dementia of uncertain etiology, one of five (20%) with primary progressive aphasia, three of five (60%) with PCA, and four of seven (57%) with possible AD were PiB positive. Brain metabolism of both PiB-positive and PiB-negative patients was generally similar by phenotype, but appeared to differ from typical AD. PCA patients also appeared to differ in their relative distribution of PiB compared with typical AD, consistent with their atypical phenotype. AD pathology is frequently present in atypical presentations of dementia and can be identified by amyloid imaging. Clinical phenotype is more related to the pattern of cerebral

  2. 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.

  3. Atypical depression among psychiatric inpatients: clinical features and personality traits.

    PubMed

    Derecho, C N; Wetzler, S; McGinn, L K; Sanderson, W C; Asnis, G M

    1996-06-20

    This study investigates the frequency and characteristics of Atypical Depression (AD) among depressed inpatients. Twenty-one depressed inpatients received DSM-IV diagnoses, were rated on the Hamilton Depression Rating Scale (HAMD), and assessed for AD using the Atypical Depressive Disorder Scale. AD was defined as the presence of mood reactivity and two of four associated features: hyperphagia, hypersomnia, leaden paralysis, rejection sensitivity. Mood reactivity was defined as the ability to reach 50% of a non-depressed mood. All subjects completed the SCL-90, MCMI-II, and a suicide survey. Seven patients (33%) met criteria for AD. AD and non-AD patients did not differ in terms of severity of depression, history of suicide attempts, levels of clinical symptomatology, age of onset of depression, prior hospitalizations, and most personality characteristics. However, AD patients scored significantly higher than non-AD patients on the SCL-90 Interpersonal Sensitivity and MCMI-II Avoidant scales, and were more likely to be single. AD is fairly prevalent on an inpatient service, comparable to the frequency found in outpatient settings. AD is not a milder form of depression. The only differences between AD and non-AD patients reflect the personality trait of rejection sensitivity which is a defining feature of AD.

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

    PubMed

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

    2015-10-01

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

  5. 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.

  6. Cytologic features of pigmented atypical meningioma mimicking melanoma on intraoperative crush preparations.

    PubMed

    Han, Song-Hee; Joo, Mee; Lee, Byung Hoon; Park, Sung-Hye

    2015-02-01

    Pigmented tumors rarely arise in the meninges, and when they do, these are mainly melanocytomas or melanomas. We describe the cytologic findings of atypical meningioma with intratumoral hemosiderin pigment mistaken for spindle cell melanoma in a 33-year-old male patient during intraoperative consultation. Preoperative radiologic images revealed a cystic meningeal mass with intratumoral hemorrhage. The crush preparation demonstrated cellular smears of syncytial clusters as well as fascicles of large pleomorphic spindle cells with discrete cytoplasmic brown pigment. Detection of cytoplasmic brown pigment and a preponderance of large spindle cells with nuclear pleomorphism led to a diagnosis of spindle cell melanoma on intraoperative cytology. Histopathologic examination displayed high cellularity, nuclear pleomorphism with prominent nucleoli, and foci of spontaneous necrosis. In addition, there were areas showing classic meningotheliomatous meningioma features. Altogether, the histologic findings were consistent with atypical meningioma. The cytoplasmic pigment in the tumor cells was confirmed to be hemosiderin using special stains and immunohistochemistry. To the best of our knowledge, this is the first case report describing cytomorphology of atypical pigmented meningioma. We discuss the differential diagnosis in intraoperative cytology and a possible mechanism related to intratumoral hemosiderin deposition in meningiomas. © 2014 Wiley Periodicals, Inc.

  7. Reclassification of cytologically atypical thyroid nodules based on radiologic features in pediatric patients.

    PubMed

    Arva, Nicoleta C; Deitch, Sarah G

    2015-07-01

    In children the percentage of "Atypia of undetermined significance/follicular lesion of undetermined significance" ("AUS/FLUS") cases is greater and the risk of malignancy is higher than expected. Our study aimed to determine if cytologically atypical nodules can be better characterized using imaging techniques for appropriate management of pediatric patients. Thyroid fine needle aspiration (FNA) specimens were reclassified using the Bethesda System for Reporting Thyroid Cytopathology (BSRTC). Cytologic-histologic correlation was performed to determine if the cytopathologic groups had different associations with the surgical outcome. The "AUS/FLUS" lesions were then subdivided based on radiologic features and the outcome was analyzed for each subgroup. Histologically benign follicular nodules showed uniform distribution between the "benign" vs. "AUS/FLUS" (p=0.09) or between the "AUS/FLUS" vs. "follicular neoplasm" ("FN") cytologic groups (p=0.27). The follicular neoplasms were also evenly distributed between the "FN" vs. "AUS/FLUS" categories (p=0.31). "Benign", "AUS/FLUS", and "FN" designations showed comparable associations with papillary thyroid carcinoma classical variant (PTC-cv). Reclassification of atypical lesions based on ultrasound findings yielded two subcategories with different risk of malignancy: one similar to the "benign" group (11% malignancy rate) and one comparable with the "FN" category (28% risk of malignant neoplasm). "AUS/FLUS" designation does not add significant value in categorization of pediatric thyroid nodules. These lesions can be reclassified based on radiologic features to provide accurate information for follow-up.

  8. 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.

  9. [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.

  10. Featured Image: Interacting Galaxies

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2017-06-01

    This beautiful image shows two galaxies, IC 2163 and NGC 2207, as they undergo a grazing collision 114 million light-years away. The image is composite, constructed from Hubble (blue), Spitzer (green), and ALMA (red) data. In a recent study, Debra Elmegreen (Vassar College) and collaborators used this ALMA data to trace the individual molecular clouds in the two interacting galaxies, identifying a total of over 200 clouds that each contain a mass of over a million solar masses. These clouds represent roughly half the molecular gas in the two galaxies total. Elmegreen and collaborators track the properties of these clouds and their relation to star-forming regions observed with Hubble. For more information about their observations, check out the paper linked below.A closer look at the ALMA observations for these galaxies, with the different emission regions labeled. Most of the molecular gas emission comes from the eyelids of IC 2163, and the nuclear ring and Feature i in NGC 2207. [Elmegreen et al. 2017]CitationDebra Meloy Elmegreen et al 2017 ApJ 841 43. doi:10.3847/1538-4357/aa6ba5

  11. Diagnosis of Fanconi anemia in children with atypical clinical features: a primary study.

    PubMed

    Liu, Rong; Hu, Tao; Li, Jun-hui; Liang, Chao; Gu, Wei-yue; Shi, Xiao-dong; Wang, Hong-xing

    2013-12-01

    Fanconi anemia is a severe congenital disorder associated with mutations in a cluster of genes responsible for DNA repair. Arriving at an accurate and timely diagnosis can be difficult in cases of Fanconi anemia with atypical clinical features. It is very important to increase the rate of accurate diagnosis for such cases in a clinical setting. The purpose of this study is to explore the clinical diagnosis of Fanconi anemia in children with atypical clinical features. Six cases of Fanconi anemia with atypical clinical features were enrolled in the study, and their clinical features were recorded, their FANCA gene transcription was assessed by RT-PCR, and FANCA mutations and the ubiquitination of FANCD2 protein were analyzed using DNA sequencing and western blotting respectively. All six cases showed atypical clinical features including no apparent deformities, lack of response to immune therapy, and progressively increasing bone marrow failure. They also have significantly increased fetal hemoglobin, negative mitomycin-induced fracture test results, and carry a FANCA gene missense mutation. Single protein ubiquitination of FANCD2 was not observed in those patients. The combination of clinical features, FANCA pathogenic gene mutation genotype and the absence of FANCD2 protein ubiquitination are helpful in the accurate and timely diagnosis of Fanconi anemia in children.

  12. 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.

  13. 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.

  14. 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

  15. Atypical features of nanophthalmic macula--a spectral domain OCT study.

    PubMed

    Rao, Aparna; Padhi, Tapas Ranjan; Jena, Sananu; Mandal, Souvik; Das, Taraprasad

    2012-06-06

    To report atypical features on Spectral domain optical coherence tomography (SD-OCT) in a case of non-familial pure adult nanophthalmos. A 39 year old male hyperope was found to have biometric and fundus findings typical of nanophthalmos. The additional atypical features included serous pigment epithelial detachment (PED) in right eye and a cuff of subretinal fluid with underlying yellow deposits along superotemporal arcade in the left eye. Fundus flourescein angiogram showed hyperfluorescence due to window defect, dye pooling due to serous PED in right eye and leak superior to disc in right eye and superotemporally in left eye. Cirrus-SD OCT horizontal line scan passing through the fovea showed extensive inner limiting membrane corrugations causing distorted foveal contour in both eyes. A large juxtafoveal serous PED and a small extrafoval PED were seen with folds in the retinal pigment epithelium (RPE)-choriocapillary layer in the right eye. Structural disruptions in the RPE-choriocapillary complex in the form of folds or juxtafoveal serous PED and RPE folds can be atypical features of nanophthalmic macula better discerned on high resolution OCT.

  16. 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.

  17. 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.

  18. [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.

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

    PubMed

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

    2014-10-01

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

  1. 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.

  2. Efficient image representations and features

    NASA Astrophysics Data System (ADS)

    Dorr, Michael; Vig, Eleonora; Barth, Erhardt

    2013-03-01

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

  3. Omental infarct: CT imaging features.

    PubMed

    Singh, A K; Gervais, D A; Lee, P; Westra, S; Hahn, P F; Novelline, R A; Mueller, P R

    2006-01-01

    The aim of this study is to describe contrast-enhanced computed tomographic (CT) features of acute omental infarction and to study the evolutionary changes on follow-up CT imaging. Fifteen cases of omental infarction were evaluated for their initial CT imaging features. The imaging features evaluated included size of the fatty lesion, location, peripheral rim, and relation to colon. CT findings were correlated with etiology, clinical presentation, and leukocytosis. Follow-up CT images were available in eight patients and the imaging features were studied. Eight omental infarcts were of unknown etiology and seven were secondary to abdominal surgery. In 53% of patients (eight of 15), the location of the omental infarct was in the right lower, mid, or upper quadrants. These eight right-side infarcts occurred in six patients with primary omental infarcts. In 13 of 14 patients who underwent CT within 15 days of onset of omental infarct, the margin of the lesion was ill defined. Primary omental (n = 8) infarcts were seen in younger patients (p = 0.02) and were larger on CT (p = 0.02) compared with secondary omental infarcts. CT findings evolved from an ill-defined, heterogeneous fat-density lesion to a well-defined, heterogeneous fat-density lesion with a peripheral hyperdense rim in all six secondary omental infarctions for which acute stage and follow-up CT images were available for interpretation. There is a significant difference in the age distribution and CT findings in terms of size of the omental infarction between primary and secondary etiologies. On follow-up CT, secondary omental infarcts progressively shrank and developed a well-defined, hyperdense rim around a fatty core.

  4. 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.

  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. 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.

  7. [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.

  8. [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

  9. 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.

  10. 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

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

    NASA Astrophysics Data System (ADS)

    Montgomery, Sharon L.; Welsh, Barry Y.

    2012-10-01

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

  12. 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.

  13. Automatic classification of atypical lymphoid B cells using digital blood image processing.

    PubMed

    Alférez, S; Merino, A; Mujica, L E; Ruiz, M; Bigorra, L; Rodellar, J

    2014-08-01

    There are automated systems for digital peripheral blood (PB) cell analysis, but they operate most effectively in nonpathological blood samples. The objective of this work was to design a methodology to improve the automatic classification of abnormal lymphoid cells. We analyzed 340 digital images of individual lymphoid cells from PB films obtained in the CellaVision DM96:150 chronic lymphocytic leukemia (CLL) cells, 100 hairy cell leukemia (HCL) cells, and 90 normal lymphocytes (N). We implemented the Watershed Transformation to segment the nucleus, the cytoplasm, and the peripheral cell region. We extracted 44 features and then the clustering Fuzzy C-Means (FCM) was applied in two steps for the lymphocyte classification. The images were automatically clustered in three groups, one of them with 98% of the HCL cells. The set of the remaining cells was clustered again using FCM and texture features. The two new groups contained 83.3% of the N cells and 71.3% of the CLL cells, respectively. The approach has been able to automatically classify with high precision three types of lymphoid cells. The addition of more descriptors and other classification techniques will allow extending the classification to other classes of atypical lymphoid cells. © 2013 John Wiley & Sons Ltd.

  14. 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).

  15. Atypical cellular blue nevi (cellular blue nevi with atypical features): lack of consensus for diagnosis and distinction from cellular blue nevi and malignant melanoma ("malignant blue nevus").

    PubMed

    Barnhill, Raymond L; Argenyi, Zsolt; Berwick, Marianne; Duray, Paul H; Erickson, Lori; Guitart, Joan; Horenstein, Marcello G; Lowe, Lori; Messina, Jane; Paine, Susan; Piepkorn, Michael W; Prieto, Victor; Rabkin, Michael S; Schmidt, Birgitta; Selim, Angelica; Shea, Chris R; Trotter, Martin J

    2008-01-01

    The distinction of cellular blue nevi (CBN) with atypical features ["atypical" CBN (ACBN)] from conventional CBN and malignant melanomas related to or derived from CBN remains a difficult problem. Here, we report on the diagnosis of various cellular blue melanocytic neoplasms by 14 dermatopathologists who routinely examine melanocytic lesions. Three parameters were assessed: (1) for between rater analyses, we calculated interobserver agreement by the kappa statistic (regardless of whether the diagnosis was correct). (2) For each individual lesion, we reported whether a majority agreement (>50%) was reached and, if so, whether the majority agreed with the gold standard diagnosis, derived from standardized histopathologic criteria for melanoma, definitive outcome such as metastatic event or death of disease, or disease-free follow-up for > or =4 years. (3) For the individual pathologists, we calculated sensitivity and specificity for each type of lesion. The study set included 26 melanocytic lesions: (1) 6 malignant melanomas developing in or with attributes of CBN; (2) 11 CBN with atypical features and indeterminate biologic potential (ACBN); (3) 8 conventional CBN; and (4) 1 common BN. The kappa values for interrater agreement varied from 0.52 (95% confidence interval 0.45, 0.58) for melanoma to 0.02 (0.05, 0.08) for ACBN and 0.20 (0.13, 0.28) for CBN. The kappa for all lesions was 0.25 (0.22, 0.28). The pathologists' sensitivities were 68.6% (61.0%, 76.1%) for melanoma, 33.1% (21.0%, 45.2%) for ACBN, and 44.6% (29.0%, 60.3%) for CBN. The specificities were 65.7% (55.8%, 75.6%) for melanoma, 84.7% (77.3%, 92.2%) for ACBN, and 89.9% (82.7%, 97.1%) for CBN. Overall, greater than 50% of the pathologists agreed and were correct in their diagnosis 38.5% (10 lesions) of the time. There was a majority agreement, but with an incorrect diagnosis, another 26.9% (7 lesions) of the time. Six of the 7 majority agreements with an incorrect diagnosis were for ACBN lesions. In

  16. Imaging features of myeloproliferative neoplasms.

    PubMed

    Murphy, I G; Mitchell, E L; Raso-Barnett, L; Godfrey, A L; Godfrey, E M

    2017-10-01

    Myeloproliferative neoplasms (MPNs) are a heterogeneous group of haematological disorders including polycythaemia vera (PV), essential thrombocythaemia (ET), primary myelofibrosis (PMF), and chronic myeloid leukaemia (CML). These disorders show large overlap in genetic and clinical presentations, and can have many different imaging manifestations. Unusual thromboses, embolic events throughout the systemic or pulmonary vasculature, or osseous findings can often be clues to the underlying disease. There is limited literature about the imaging features of these disorders, and this may result in under-diagnosis. Multiple treatments are available for symptom control, and the development of multiple new pharmacological inhibitors has significantly improved morbidity and prognosis. Knowledge of these conditions may enable the radiologist to suggest an MPN as a possible underlying cause for certain imaging findings, particularly unexplained splanchnic venous thrombosis, i.e. in the absence of chronic liver disease or pancreatitis. The aim of the present review is to outline using examples the different categories of MPN and illustrate the variety of radiological findings associated with these diseases. Copyright © 2017 The Royal College of Radiologists. All rights reserved.

  17. Understanding the Uncanny: Both Atypical Features and Category Ambiguity Provoke Aversion toward Humanlike Robots.

    PubMed

    Strait, Megan K; Floerke, Victoria A; Ju, Wendy; Maddox, Keith; Remedios, Jessica D; Jung, Malte F; Urry, Heather L

    2017-01-01

    Robots intended for social contexts are often designed with explicit humanlike attributes in order to facilitate their reception by (and communication with) people. However, observation of an "uncanny valley"-a phenomenon in which highly humanlike entities provoke aversion in human observers-has lead some to caution against this practice. Both of these contrasting perspectives on the anthropomorphic design of social robots find some support in empirical investigations to date. Yet, owing to outstanding empirical limitations and theoretical disputes, the uncanny valley and its implications for human-robot interaction remains poorly understood. We thus explored the relationship between human similarity and people's aversion toward humanlike robots via manipulation of the agents' appearances. To that end, we employed a picture-viewing task (Nagents = 60) to conduct an experimental test (Nparticipants = 72) of the uncanny valley's existence and the visual features that cause certain humanlike robots to be unnerving. Across the levels of human similarity, we further manipulated agent appearance on two dimensions, typicality (prototypic, atypical, and ambiguous) and agent identity (robot, person), and measured participants' aversion using both subjective and behavioral indices. Our findings were as follows: (1) Further substantiating its existence, the data show a clear and consistent uncanny valley in the current design space of humanoid robots. (2) Both category ambiguity, and more so, atypicalities provoke aversive responding, thus shedding light on the visual factors that drive people's discomfort. (3) Use of the Negative Attitudes toward Robots Scale did not reveal any significant relationships between people's pre-existing attitudes toward humanlike robots and their aversive responding-suggesting positive exposure and/or additional experience with robots is unlikely to affect the occurrence of an uncanny valley effect in humanoid robotics. This work furthers our

  18. Understanding the Uncanny: Both Atypical Features and Category Ambiguity Provoke Aversion toward Humanlike Robots

    PubMed Central

    Strait, Megan K.; Floerke, Victoria A.; Ju, Wendy; Maddox, Keith; Remedios, Jessica D.; Jung, Malte F.; Urry, Heather L.

    2017-01-01

    Robots intended for social contexts are often designed with explicit humanlike attributes in order to facilitate their reception by (and communication with) people. However, observation of an “uncanny valley”—a phenomenon in which highly humanlike entities provoke aversion in human observers—has lead some to caution against this practice. Both of these contrasting perspectives on the anthropomorphic design of social robots find some support in empirical investigations to date. Yet, owing to outstanding empirical limitations and theoretical disputes, the uncanny valley and its implications for human-robot interaction remains poorly understood. We thus explored the relationship between human similarity and people's aversion toward humanlike robots via manipulation of the agents' appearances. To that end, we employed a picture-viewing task (Nagents = 60) to conduct an experimental test (Nparticipants = 72) of the uncanny valley's existence and the visual features that cause certain humanlike robots to be unnerving. Across the levels of human similarity, we further manipulated agent appearance on two dimensions, typicality (prototypic, atypical, and ambiguous) and agent identity (robot, person), and measured participants' aversion using both subjective and behavioral indices. Our findings were as follows: (1) Further substantiating its existence, the data show a clear and consistent uncanny valley in the current design space of humanoid robots. (2) Both category ambiguity, and more so, atypicalities provoke aversive responding, thus shedding light on the visual factors that drive people's discomfort. (3) Use of the Negative Attitudes toward Robots Scale did not reveal any significant relationships between people's pre-existing attitudes toward humanlike robots and their aversive responding—suggesting positive exposure and/or additional experience with robots is unlikely to affect the occurrence of an uncanny valley effect in humanoid robotics. This work

  19. Predictive features of chronic kidney disease in atypical haemolytic uremic syndrome

    PubMed Central

    Jamme, Matthieu; Raimbourg, Quentin; Chauveau, Dominique; Seguin, Amélie; Presne, Claire; Perez, Pierre; Gobert, Pierre; Wynckel, Alain; Provôt, François; Delmas, Yahsou; Mousson, Christiane; Servais, Aude; Vrigneaud, Laurence; Veyradier, Agnès

    2017-01-01

    Chronic kidney disease (CKD) is a frequent and serious complication of atypical haemolytic uremic syndrome (aHUS). We aimed to develop a simple accurate model to predict the risk of renal dysfunction in aHUS based on clinical and biological features available at hospital admission. Renal function at 1-year follow-up, based on an estimated glomerular filtration rate < 60mL/min/1.73m2 as assessed by the Modification of Diet in Renal Disease equation, was used as an indicator of significant CKD. Prospectively collected data from a cohort of 156 aHUS patients who did not receive eculizumab were used to identify predictors of CKD. Covariates associated with renal impairment were identified by multivariate analysis. The model performance was assessed and a scoring system for clinical practice was constructed from the regression coefficient. Multivariate analyses identified three predictors of CKD: a high serum creatinine level, a high mean arterial pressure and a mildly decreased platelet count. The prognostic model had a good discriminative ability (area under the curve = .84). The scoring system ranged from 0 to 5, with corresponding risks of CKD ranging from 18% to 100%. This model accurately predicts development of 1-year CKD in patients with aHUS using clinical and biological features available on admission. After further validation, this model may assist in clinical decision making. PMID:28542627

  20. 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.

  1. "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.

  2. Leptin Dysregulation Is Specifically Associated With Major Depression With Atypical Features: Evidence for a Mechanism Connecting Obesity and Depression.

    PubMed

    Milaneschi, Yuri; Lamers, Femke; Bot, Mariska; Drent, Madeleine L; Penninx, Brenda W J H

    2017-05-01

    Obesity-related dysregulation of leptin signaling (e.g., hyperleptinemia due to central functional resistance) may affect mood. However, evidence for leptin dysregulation in major depressive disorder (MDD) is conflicting. Inconclusive findings may be attributable to heterogeneity of MDD, aggregating biologically different subtypes. We examined the relationship of leptin with MDD, its common subtypes (typical and atypical), and clinical features. The sample consisted of participants (aged 18 to 65 years) from the Netherlands Study of Depression and Anxiety with current (n = 1062) or remitted (n = 711) MDD and healthy control subjects (n = 497). Diagnoses of MDD and subtypes were based on DSM-IV symptoms. Additional symptoms were measured with the Inventory of Depressive Symptomatology. Blood levels of leptin and adiposity indexes (body mass index and waist circumference) were assessed. As compared to control subjects, higher leptin was associated with the atypical MDD subtype both for remitted (n = 144, odds ratio = 1.53, 95% confidence interval = 1.16-2.03, p = .003) and current (n = 270, odds ratio = 1.90, 95% confidence interval = 1.51-2.93, p = 5.3e-8) cases. This association was stronger for increasing adiposity levels (leptin by body mass index interaction, p < .02), strengthening the hypothesis of the involvement of leptin resistance. No association with leptin was found for overall MDD or the typical subtype. Among currently depressed patients, higher leptin was associated with key symptoms identifying the atypical subtype, such as hyperphagia, increased weight, and leaden paralysis. Leptin dysregulation (resistance) may represent an underlying mechanism connecting obesity and MDD with atypical features. Development of treatment effectively targeting leptin resistance may benefit patients with atypical depression characterized by obesity-related metabolic alterations. Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights

  3. 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.

  4. 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.

  5. [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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. Featured Image: A Double Cluster

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2015-11-01

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

  11. 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.

  12. 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.

  13. 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

  14. Finding curvilinear features in speckled images

    NASA Technical Reports Server (NTRS)

    Samadani, Ramin; Vesecky, John F.

    1990-01-01

    A method for finding curves in digital images with speckle noise is described. The solution method differs from standard linear convolutions followed by thresholds in that it explicitly allows curvature in the features. Maximum a posteriori (MAP) estimation is used, together with statistical models for the speckle noise and for the curve-generation process, to find the most probable estimate of the feature, given the image data. The estimation process is first described in general terms. Then, incorporation of the specific neighborhood system and a multiplicative noise model for speckle allows derivation of the solution, using dynamic programming, of the estimation problem. The detection of curvilinear features is considered separately. The detection results allow the determination of the minimal size of detectable feature. Finally, the estimation of linear features, followed by a detection step, is shown for computer-simulated images and for a SAR image of sea ice.

  15. 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.

  16. 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

  17. Featured Image: Identifying Weird Galaxies

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2017-08-01

    Hoags Object, an example of a ring galaxy. [NASA/Hubble Heritage Team/Ray A. Lucas (STScI/AURA)]The above image (click for the full view) shows PanSTARRSobservationsof some of the 185 galaxies identified in a recent study as ring galaxies bizarre and rare irregular galaxies that exhibit stars and gas in a ring around a central nucleus. Ring galaxies could be formed in a number of ways; one theory is that some might form in a galaxy collision when a smaller galaxy punches through the center of a larger one, triggering star formation around the center. In a recent study, Ian Timmis and Lior Shamir of Lawrence Technological University in Michigan explore ways that we may be able to identify ring galaxies in the overwhelming number of images expected from large upcoming surveys. They develop a computer analysis method that automatically finds ring galaxy candidates based on their visual appearance, and they test their approach on the 3 million galaxy images from the first PanSTARRS data release. To see more of the remarkable galaxies the authors found and to learn more about their identification method, check out the paper below.CitationIan Timmis and Lior Shamir 2017 ApJS 231 2. doi:10.3847/1538-4365/aa78a3

  18. 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.

  19. 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.

  20. 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

  1. 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.

  2. 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.

  3. A systematic review of lessons learned from PET molecular imaging research in atypical parkinsonism.

    PubMed

    Niccolini, Flavia; Politis, Marios

    2016-11-01

    To systematically review the previous studies and current status of positron emission tomography (PET) molecular imaging research in atypical parkinsonism. MEDLINE, ISI Web of Science, Cochrane Library, and Scopus electronic databases were searched for articles published until 29th March 2016 and included brain PET studies in progressive supranuclear palsy (PSP), multiple system atrophy (MSA), and corticobasal syndrome (CBS). Only articles published in English and in peer-reviewed journals were included in this review. Case-reports, reviews, and non-human studies were excluded. Seventy-seven PET studies investigating the dopaminergic system, glucose metabolism, microglial activation, hyperphosphorilated tau, opioid receptors, the cholinergic system, and GABAA receptors in PSP, MSA, and CBS patients were included in this review. Disease-specific patterns of reduced glucose metabolism have shown higher accuracy than dopaminergic imaging techniques to distinguish between parkinsonian syndromes. Microglial activation has been found in all forms of atypical parkinsonism and reflects the known distribution of neuropathologic changes in these disorders. Opioid receptors are decreased in the striatum of PSP and MSA patients. Subcortical cholinergic dysfunction was more severe in MSA and PSP than Parkinson's disease patients although no significant changes in cortical cholinergic receptors were seen in PSP with cognitive impairment. GABAA receptors were decreased in metabolically affected cortical and subcortical regions in PSP patients. PET molecular imaging has provided valuable insight for understanding the mechanisms underlying atypical parkinsonism. Changes at a molecular level occur early in the course of these neurodegenerative diseases and PET imaging provides the means to aid differential diagnosis, monitor disease progression, identify of novel targets for pharmacotherapy, and monitor response to new treatments.

  4. The role of diffusion magnetic resonance imaging in Parkinson's disease and in the differential diagnosis with atypical parkinsonism

    PubMed Central

    de Oliveira, Romulo Varella; Pereira, João Santos

    2017-01-01

    Parkinson's disease is one of the most common neurodegenerative diseases. Clinically, it is characterized by motor symptoms. Parkinson's disease should be differentiated from atypical parkinsonism conditions. Conventional magnetic resonance imaging is the primary imaging method employed in order to facilitate the differential diagnosis, and its role has grown after the development of advanced techniques such as diffusion-weighted imaging. The purpose of this article was to review the role of magnetic resonance imaging in Parkinson's disease and in the differential diagnosis with atypical parkinsonism, emphasizing the diffusion technique. PMID:28894333

  5. 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.

  6. 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

  7. [Clinical features of four atypical pediatric cases of endemic typhus with pneumonia].

    PubMed

    Liu, Jin-rong; Xu, Bao-ping; Li, Shao-gang; Liu, Jun; Tian, Bao-lin; Zhao, Shun-ying

    2013-10-01

    To analyze clinical manifestations, treatment and prognosis of 4 cases with endemic typhus. The clinical data of four endemic typhus patients in prognosis were retrospectively analyzed. These four atypical cases of endemic typhus with pneumonia were treated in our department from October 2011 to March 2012. They were all male, with an age range of 15 months to 7 years. The four patients had long history, mild respiratory symptom and no improvement was found after treatment with cephalosporins. There were no evidences of bacterial, viral, or fungal infections and we thought they might have infection with other pathogen. Three were from rural areas. Routine blood tests, Weil-Felix reaction, blood smear (Giemsa staining) , and indirect immunofluorescence assay were performed. Blood smear and IFA tests showed evidences for endemic typhus. The clinical presentations were atypical, the patients had no headache, but all had fever, rash, and pneumonia of varying severity. None of the patients had a severe cough, but bronchial casts were observed in one case. Recurrent fever was reported in three cases. Physical examinations showed no eschars, but one patient had a subconjunctival hemorrhage, and one had skin scratches, cervical lymphadenopathy, pleural effusion, pericardial effusion, and cardiac dilatation. Two patients had remarkably increased peripheral blood leukocyte counts; both these patients also had high alanine aminotransferase (ALT) levels and one had a high C-reactive protein (CRP) level. Weil-Felix testing was negative or the OX19 titer was low. The peripheral blood smear (Giemsa stain) showed intracellular pathogens in all four cases. After combined therapy with doxycycline and macrolide antibiotics, all four patients recovered well. The endemic typhus children often come from rural areas. The clinical presentations were atypical, they usually have no headache, but have fever (often Periodic fever) , rash, and pneumonia of varying severity in these four cases

  8. 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.

  9. 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.

  10. Imaging features of haematological malignancies of kidneys.

    PubMed

    Sandrasegaran, K; Menias, C O; Verma, S; Abdelbaki, A; Shaaban, A; Elsayes, K M

    2016-03-01

    Haematological malignancies are relatively uncommon neoplasms of kidneys. Nevertheless, the incidence of these neoplasms is increasing, partly due to more widespread use of computed tomography and magnetic resonance imaging. This article discusses the clinical and imaging features of renal lymphoma, leukaemia, extra-osseous multiple myeloma, and post-transplant lymphoproliferative disorder. Although there is overlap of imaging features with other more common malignancies, such as transitional and renal cell cancers, the combination of imaging findings and the appropriate clinical picture should allow the radiologist to raise a provisional diagnosis of a haematological neoplasm. This has management implications including the preference for image-guided core biopsies and a shift towards medical rather than surgical therapy. Copyright © 2015 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

  11. Immediate laparoscopic adrenalectomy versus observation: cost evaluation for incidental adrenal lesions with atypical imaging characteristics.

    PubMed

    Melck, Adrienne L; Rosengart, Matthew R; Armstrong, Michaele J; Stang, Michael T; Carty, Sally E; Yip, Linwah

    2012-10-01

    Because of controversy in the management of nonfunctional adrenal masses <6 cm with lipid-poor imaging characteristics, the study was conducted to compare the costs of observation versus immediate laparoscopic adrenalectomy. A total of 370 patients who were evaluated for incidental adrenal masses between January 1999 and December 2007 were identified, and 32 (8.7%) patients had lesions with imaging characteristics that were inconsistent with a benign adenoma (ie, atypical appearing). Sixteen patients underwent immediate surgery and 16 had observation with serial imaging and biochemical studies. The associated total costs were subjected to intention-to-treat analysis. In the observation cohort, 7 patients converted and underwent adrenalectomy after a mean of 13.1 months. Initially, costs of immediate surgery exceeded those of observation ($12,015.72 vs $11,601.18, P = .10). After projecting costs of annual surveillance, a cost advantage for immediate surgery was demonstrated after 9 years (P = .02). In patients with <6 cm atypical-appearing adrenal lesions, the costs of surgery and of observation are initially equal. After 9 years, the costs of surveillance exceed that of initial laparoscopic adrenalectomy. Copyright © 2012 Elsevier Inc. All rights reserved.

  12. Hemorrhage detection in MRI brain images using images features

    NASA Astrophysics Data System (ADS)

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

    2013-11-01

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

  13. 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.

  14. 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)

  15. 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.

  16. 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.

  17. 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

  18. 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.

  19. 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…

  20. Atypical Cities

    ERIC Educational Resources Information Center

    DiJulio, Betsy

    2011-01-01

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

  1. Atypical Fibroxanthoma.

    PubMed

    López, Liurka; Vélez, Román

    2016-04-01

    Atypical fibroxanthoma is a malignant skin tumor with histologic features similar to those of undifferentiated pleomorphic sarcoma, but lacking its more aggressive behavior. The tumor is composed of pleomorphic cells with hyperchromatic nuclei and abundant cytoplasm, commonly arranged in a spindle cell pattern. Recent genetic studies have identified similarities between atypical fibroxanthoma and undifferentiated pleomorphic sarcoma, such as the presence of 9p and 13q deletions in both tumors, favoring a common histogenesis. However, the lack of K-ras and H-ras mutations in atypical fibroxanthoma compared with undifferentiated pleomorphic sarcoma could explain the difference in aggressiveness and continued separation of these entities. Exclusion of other neoplasms by histology and immunohistochemistry followed by complete surgical removal remains the standard of care.

  2. 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.

  3. [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.

  4. 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.

  5. Disorders of cortical formation: MR imaging features.

    PubMed

    Abdel Razek, A A K; Kandell, A Y; Elsorogy, L G; Elmongy, A; Basett, A A

    2009-01-01

    The purpose of this article was to review the embryologic stages of the cerebral cortex, illustrate the classification of disorders of cortical formation, and finally describe the main MR imaging features of these disorders. Disorders of cortical formation are classified according to the embryologic stage of the cerebral cortex at which the abnormality occurred. MR imaging shows diminished cortical thickness and sulcation in microcephaly, enlarged dysplastic cortex in hemimegalencephaly, and ipsilateral focal cortical thickening with radial hyperintense bands in focal cortical dysplasia. MR imaging detects smooth brain in classic lissencephaly, the nodular cortex with cobblestone cortex with congenital muscular dystrophy, and the ectopic position of the gray matter with heterotopias. MR imaging can detect polymicrogyria and related syndromes as well as the types of schizencephaly. We concluded that MR imaging is essential to demonstrate the morphology, distribution, and extent of different disorders of cortical formation as well as the associated anomalies and related syndromes.

  6. 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.

  7. 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.

  8. 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.

  9. Multispectral image fusion based on fractal features

    NASA Astrophysics Data System (ADS)

    Tian, Jie; Chen, Jie; Zhang, Chunhua

    2004-01-01

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

  10. 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

  11. 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

  12. Magnetic Resonance Imaging Features of Solitary Hypothalamitis.

    PubMed

    Zhang, Hua; Wang, Jing; Wu, Yue; Tang, Ying; Tao, Ran; Ye, Hongying; Yao, Zhenwei

    The study aimed to characterize magnetic resonance imaging (MRI) findings of solitary hypothalamitis and evaluate their clinical value in diagnosis. Magnetic resonance imaging scans, including T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and contrast-enhanced T1-weighted sequences, of 8 biopsy-proven hypothalamitis lesions were retrospectively analyzed along with MRI features including size, shape, signal intensity, enhancement pattern, correlation with adjacent tissues, and changes in infundibular stalk and sella turcica. Of 8 patients, 5 were diagnosed with lymphoplasmacytic proliferative inflammation, 2 with Langerhans cell histocytosis, and 1 with Rosai-Dorfman disease. Solitary hypothalamitis predominantly demonstrated mild hypointensity/isointensity in T1WI and mild hyperintensity in T2-weighted imaging. In contrast-enhanced T1WI, all lesions showed heterogeneous but primarily peripheral enhancement patterns. Seven cases showed the polygon sign. In T1WI, the normal high signal intensity of neurohypophysis was absent from all patients, with no infundibular stalk thickening. Seven patients presented with optic chiasma edema, and 5 with edema-like changes along the optic tract (OTE), but most showed no visual impairment (n = 7). Magnetic resonance imaging, particularly postcontrast MRI, is the optimal modality for assessment of hypothalamic lesions. Peripheral enhancement with polygon sign and optic tract or chiasm edema without visual impairment are highly suggestive of hypothalamitis.

  13. Gestational Trophoblastic Disease: Clinical and Imaging Features.

    PubMed

    Shaaban, Akram M; Rezvani, Maryam; Haroun, Reham R; Kennedy, Anne M; Elsayes, Khaled M; Olpin, Jeffrey D; Salama, Mohamed E; Foster, Bryan R; Menias, Christine O

    2017-01-01

    Gestational trophoblastic disease (GTD) is a spectrum of both benign and malignant gestational tumors, including hydatidiform mole (complete and partial), invasive mole, choriocarcinoma, placental site trophoblastic tumor, and epithelioid trophoblastic tumor. The latter four entities are referred to as gestational trophoblastic neoplasia (GTN). These conditions are aggressive with a propensity to widely metastasize. GTN can result in significant morbidity and mortality if left untreated. Early diagnosis of GTD is essential for prompt and successful management while preserving fertility. Initial diagnosis of GTD is based on a multifactorial approach consisting of clinical features, serial quantitative human chorionic gonadotropin (β-hCG) titers, and imaging findings. Ultrasonography (US) is the modality of choice for initial diagnosis of complete hydatidiform mole and can provide an invaluable means of local surveillance after treatment. The performance of US in diagnosing all molar pregnancies is surprisingly poor, predominantly due to the difficulty in differentiating partial hydatidiform mole from nonmolar abortion and retained products of conception. While GTN after a molar pregnancy is usually diagnosed with serial β-hCG titers, imaging plays an important role in evaluation of local extent of disease and systemic surveillance. Imaging also plays a crucial role in detection and management of complications, such as uterine and pulmonary arteriovenous fistulas. Familiarity with the pathogenesis, classification, imaging features, and treatment of these tumors can aid in radiologic diagnosis and guide appropriate management. (©)RSNA, 2017.

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

    SciTech Connect

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

    1996-09-01

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

  15. 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.

  16. 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.

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

    PubMed Central

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

    2016-01-01

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

  18. 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.

  19. 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.

  20. 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.

  1. Feature selection with the image grand tour

    NASA Astrophysics Data System (ADS)

    Marchette, David J.; Solka, Jeffrey L.

    2000-08-01

    The grand tour is a method for visualizing high dimensional data by presenting the user with a set of projections and the projected data. This idea was extended to multispectral images by viewing each pixel as a multidimensional value, and viewing the projections of the grand tour as an image. The user then looks for projections which provide a useful interpretation of the image, for example, separating targets from clutter. We discuss a modification of this which allows the user to select convolution kernels which provide useful discriminant ability, both in an unsupervised manner as in the image grand tour, or in a supervised manner using training data. This approach is extended to other window-based features. For example, one can define a generalization of the median filter as a linear combination of the order statistics within a window. Thus the median filter is that projection containing zeros everywhere except for the middle value, which contains a one. Using the convolution grand tour one can select projections on these order statistics to obtain new nonlinear filters.

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

    PubMed

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

    2015-04-01

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

  3. 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

  4. A comparative analysis of magnetic resonance imaging and radiographic examinations of patients with atypical odontalgia.

    PubMed

    Pigg, Maria; List, Thomas; Abul-Kasim, Kasim; Maly, Pavel; Petersson, Arne

    2014-01-01

    To examine (1) the occurrence of magnetic resonance imaging (MRI) signal changes in the painful regions of patients with atypical odontalgia (AO) and (2) the correlation of such findings to periapical bone defects detected with a comprehensive radiographic examination including cone beam computed tomography (CBCT). A total of 20 patients (mean age 52 years, range 34 to 65) diagnosed with AO participated. Mean pain intensity (± standard deviation) was 5.6 ± 1.8 on a 0-10 numerical rating scale, and mean pain duration was 4.3 ± 5.2 years. The inclusion criterion was chronic pain (> 6 months) located in a region with no clear pathologic cause identified clinically or in periapical radiographs. In addition to a clinical examination and a self-report questionnaire, the assessments included radiographic examinations (panoramic, periapical, and CBCT images), and an MRI examination. Changes in MRI signal in the painful region were recorded. Spearman's rank correlation between radiographic and MRI findings was calculated. Eight of the patients (40%) had MRI signal changes in the pain region. The correlation to radiographic periapical radiolucencies was 0.526 (P = .003). Of the eight teeth displaying changes in MRI signal, six showed periapical radiolucency in the radiographs. MRI examination revealed no changes in the painful region in a majority of patients with AO, suggesting that inflammation was not present. MRI findings were significantly correlated to radiographic findings.

  5. 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…

  6. 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…

  7. Unsupervised feature learning for autonomous rock image classification

    NASA Astrophysics Data System (ADS)

    Shu, Lei; McIsaac, Kenneth; Osinski, Gordon R.; Francis, Raymond

    2017-09-01

    Autonomous rock image classification can enhance the capability of robots for geological detection and enlarge the scientific returns, both in investigation on Earth and planetary surface exploration on Mars. Since rock textural images are usually inhomogeneous and manually hand-crafting features is not always reliable, we propose an unsupervised feature learning method to autonomously learn the feature representation for rock images. In our tests, rock image classification using the learned features shows that the learned features can outperform manually selected features. Self-taught learning is also proposed to learn the feature representation from a large database of unlabelled rock images of mixed class. The learned features can then be used repeatedly for classification of any subclass. This takes advantage of the large dataset of unlabelled rock images and learns a general feature representation for many kinds of rocks. We show experimental results supporting the feasibility of self-taught learning on rock images.

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

    PubMed

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

    2016-06-01

    The atypical chemokine receptor ACKR3 (formerly CXCR7), overexpressed in various cancers compared with 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 marker for noninvasive 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 noninvasive in vivo nuclear imaging of ACKR3 expression in human breast, lung, and esophageal squamous cell carcinoma cancer xenografts. ACKR3 expression data were extracted from Cancer Cell Line Encyclopedia, The Cancer Genome Atlas, and the Clinical Lung Cancer Genome Project. (89)Zr-ACKR3-mAb was evaluated in vitro and subsequently in vivo by PET and ex vivo biodistribution studies in mice xenografted with breast (MDA-MB-231-ACKR3 [231-ACKR3], MDA-MB-231 [231], MCF7), lung (HCC95), or esophageal (KYSE520) cancer cells. In addition, ACKR3-mAb was radiolabeled with (125)I and evaluated by SPECT imaging and ex vivo biodistribution studies. ACKR3 transcript levels were highest in lung squamous cell carcinoma among the 21 cancer type data extracted from The Cancer Genome Atlas. Also, Clinical Lung Cancer Genome Project data showed that lung squamous cell carcinoma had the highest CXCR7 transcript levels compared with other lung cancer subtypes. The (89)Zr-ACKR3-mAb was produced in 80% ± 5% radiochemical yields with greater than 98% radiochemical purity. In vitro cell uptake of (89)Zr-ACKR3-mAb correlated with gradient levels of cell surface ACKR3 expression observed by flow cytometry. In vivo PET imaging

  9. 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

  10. 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

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

    PubMed

    Wang, Yi-Chun; Chou, Chen-Te; Lin, Ching-Po; Chen, Yao-Li; Chen, Yung-Fang; Chen, Ran-Chou

    2017-01-01

    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. 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. 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. 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.

  12. 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

  13. 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.

  14. 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.

  15. 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

  16. Image ratio features for facial expression recognition application.

    PubMed

    Song, Mingli; Tao, Dacheng; Liu, Zicheng; Li, Xuelong; Zhou, Mengchu

    2010-06-01

    Video-based facial expression recognition is a challenging problem in computer vision and human-computer interaction. To target this problem, texture features have been extracted and widely used, because they can capture image intensity changes raised by skin deformation. However, existing texture features encounter problems with albedo and lighting variations. To solve both problems, we propose a new texture feature called image ratio features. Compared with previously proposed texture features, e.g., high gradient component features, image ratio features are more robust to albedo and lighting variations. In addition, to further improve facial expression recognition accuracy based on image ratio features, we combine image ratio features with facial animation parameters (FAPs), which describe the geometric motions of facial feature points. The performance evaluation is based on the Carnegie Mellon University Cohn-Kanade database, our own database, and the Japanese Female Facial Expression database. Experimental results show that the proposed image ratio feature is more robust to albedo and lighting variations, and the combination of image ratio features and FAPs outperforms each feature alone. In addition, we study asymmetric facial expressions based on our own facial expression database and demonstrate the superior performance of our combined expression recognition system.

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

    PubMed Central

    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 and frontal brain regions. Autism and sibling groups, however, did not differ in terms of activation during this task. This suggests that the pattern of atypical activation identified may represent a functional endophenotype of autism, related to familial risk for the condition shared between individuals with autism and their siblings. We also found that reduced activation in autism relative to control subjects in regions including associative visual and face processing areas was strongly correlated with the clinical severity of impairments in reciprocal social interaction. Behavioural performance was intact in autism and sibling groups. Results are discussed in terms of atypical information processing styles or of increased activation in temporal and frontal regions in autism and the broader phenotype. By separating the aspects of atypical activation as markers of familial risk for the condition from those that are autism-specific, our findings offer new insight into the factors that might cause the expression of autism in families, affecting some children but not others. PMID:23065480

  18. 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

  19. 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

  20. 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.

  1. Atypical fibroxanthoma†

    PubMed Central

    Zogbi, Luciano; Juliano, Camila; Neutzling, Aluísio

    2015-01-01

    Atypical fibroxanthoma (AFX) is a rare skin neoplasm of low-grade malignancy and fibroblastic origin. AFX is a curable cutaneous disease and the diagnosis depends on knowledge of its clinical and histological features and combined immunohistochemistry markers. This study presents a case of a male patient, aged 90 years, presented with painless skin lesion in his ear. The lesion had been growing progressively for 2 months, measured ∼1.5 cm, ulcerated, fixed and firm. After a biopsy, the patient underwent a complete resection with adequate surgical margins and showed favorable evolution without complications or recurrence. The histopathological evaluation showed a poorly circumscribed ulcerated dermal nodule, mesenchymal proliferation, with pleomorphic spindle cells. There was infiltration of the deep dermis and subcutis, showing malignant features, but there was no invasion of cartilage. The immunohistochemical analysis confirmed the diagnosis of AFX. PMID:25742967

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. Combined Diffusion Tensor Imaging and Apparent Transverse Relaxation Rate Differentiate Parkinson Disease and Atypical Parkinsonism.

    PubMed

    Du, G; Lewis, M M; Kanekar, S; Sterling, N W; He, L; Kong, L; Li, R; Huang, X

    2017-05-01

    Both diffusion tensor imaging and the apparent transverse relaxation rate have shown promise in differentiating Parkinson disease from atypical parkinsonism (particularly multiple system atrophy and progressive supranuclear palsy). The objective of the study was to assess the ability of DTI, the apparent transverse relaxation rate, and their combination for differentiating Parkinson disease, multiple system atrophy, progressive supranuclear palsy, and controls. A total of 106 subjects (36 controls, 35 patients with Parkinson disease, 16 with multiple system atrophy, and 19 with progressive supranuclear palsy) were included. DTI and the apparent transverse relaxation rate measures from the striatal, midbrain, limbic, and cerebellar regions were obtained and compared among groups. The discrimination performance of DTI and the apparent transverse relaxation rate among groups was assessed by using Elastic-Net machine learning and receiver operating characteristic curve analysis. Compared with controls, patients with Parkinson disease showed significant apparent transverse relaxation rate differences in the red nucleus. Compared to those with Parkinson disease, patients with both multiple system atrophy and progressive supranuclear palsy showed more widespread changes, extending from the midbrain to striatal and cerebellar structures. The pattern of changes, however, was different between the 2 groups. For instance, patients with multiple system atrophy showed decreased fractional anisotropy and an increased apparent transverse relaxation rate in the subthalamic nucleus, whereas patients with progressive supranuclear palsy showed an increased mean diffusivity in the hippocampus. Combined, DTI and the apparent transverse relaxation rate were significantly better than DTI or the apparent transverse relaxation rate alone in separating controls from those with Parkinson disease/multiple system atrophy/progressive supranuclear palsy; controls from those with Parkinson

  7. Enhancement of features in galaxy images

    NASA Technical Reports Server (NTRS)

    Djorgovski, S.

    1986-01-01

    Several image-enhancement techniques useful for morphological analysis of galactic or cometary images are described and compared. Such techniques can be used to search for, and investigate the properties of dust lanes, stellar disks or rings, jets, shells, tidal distortions, etc. Applications of the techniques are illustrated on CCD images of the peculiar galaxy Arp 230; this object has a rich morphology, indicative of a merger of two disk galaxies.

  8. 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

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

    SciTech Connect

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

    2012-01-16

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

  10. 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.

  11. 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.

  12. Computer detection of features in biomedical images

    SciTech Connect

    Not Available

    1993-05-01

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

  13. 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…

  14. 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.

  15. 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.

  16. 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

  17. 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.

  18. Featured Image: A New Look at Fomalhaut

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2017-06-01

    ALMA continuum image overlaid as contours on the Hubble STIS image of Fomalhaut. [MacGregor et al. 2017]This stunning image of the Fomalhaut star system was taken by the Atacama Large Millimeter/submillimeter Array (ALMA) in Chile. This image maps the 1.3-mm continuum emission from the dust around the central star, revealing a ring that marks the outer edge of the planet-forming debris disk surrounding the star. In a new study, a team of scientists led by Meredith MacGregor (Harvard-Smithsonian Center for Astrophysics) examines these ALMA observations of Fomalhaut, which beautifully complement former Hubble images of the system. ALMAs images provide the first robust detection of apocenter glow the brightening of the ring at the point farthest away from the central star, a side effect of the rings large eccentricity. The authors use ALMAsobservations to measure properties of the disk, such as its span (roughly 136 x 14 AU), eccentricity (e 0.12), and inclination angle ( 66). They then explore the implications for Fomalhaut b, the planet located near the outer disk. To read more about the teams observations, check out the paper below.CitationMeredith A. MacGregor et al 2017 ApJ 842 8. doi:10.3847/1538-4357/aa71ae

  19. Skin image retrieval using Gabor wavelet texture feature.

    PubMed

    Ou, X; Pan, W; Zhang, X; Xiao, P

    2016-12-01

    Skin imaging plays a key role in many clinical studies. We have used many skin imaging techniques, including the recently developed capacitive contact skin imaging based on fingerprint sensors. The aim of this study was to develop an effective skin image retrieval technique using Gabor wavelet transform, which can be used on different types of skin images, but with a special focus on skin capacitive contact images. Content-based image retrieval (CBIR) is a useful technology to retrieve stored images from database by supplying query images. In a typical CBIR, images are retrieved based on colour, shape, texture, etc. In this study, texture feature is used for retrieving skin images, and Gabor wavelet transform is used for texture feature description and extraction. The results show that the Gabor wavelet texture features can work efficiently on different types of skin images. Although Gabor wavelet transform is slower compared with other image retrieval techniques, such as principal component analysis (PCA) and grey-level co-occurrence matrix (GLCM), Gabor wavelet transform is the best for retrieving skin capacitive contact images and facial images with different orientations. Gabor wavelet transform can also work well on facial images with different expressions and skin cancer/disease images. We have developed an effective skin image retrieval method based on Gabor wavelet transform, that it is useful for retrieving different types of images, namely digital colour face images, digital colour skin cancer and skin disease images, and particularly greyscale skin capacitive contact images. Gabor wavelet transform can also be potentially useful for face recognition (with different orientation and expressions) and skin cancer/disease diagnosis. © 2016 Society of Cosmetic Scientists and the Société Française de Cosmétologie.

  20. Feature-based Alignment of Volumetric Multi-modal Images

    PubMed Central

    Toews, Matthew; Zöllei, Lilla; Wells, William M.

    2014-01-01

    This paper proposes a method for aligning image volumes acquired from different imaging modalities (e.g. MR, CT) based on 3D scale-invariant image features. A novel method for encoding invariant feature geometry and appearance is developed, based on the assumption of locally linear intensity relationships, providing a solution to poor repeatability of feature detection in different image modalities. The encoding method is incorporated into a probabilistic feature-based model for multi-modal image alignment. The model parameters are estimated via a group-wise alignment algorithm, that iteratively alternates between estimating a feature-based model from feature data, then realigning feature data to the model, converging to a stable alignment solution with few pre-processing or pre-alignment requirements. The resulting model can be used to align multi-modal image data with the benefits of invariant feature correspondence: globally optimal solutions, high efficiency and low memory usage. The method is tested on the difficult RIRE data set of CT, T1, T2, PD and MP-RAGE brain images of subjects exhibiting significant inter-subject variability due to pathology. PMID:24683955

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

    NASA Astrophysics Data System (ADS)

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

    2015-09-01

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

  2. 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

  3. 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.

  4. 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

  5. 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

  6. Cerebellopontine angle tumors in young children, displaying cranial nerve deficits, and restricted diffusion on diffusion-weighted imaging: a new clinical triad for atypical teratoid/rhabdoid tumors.

    PubMed

    Katz, Joel S; Peruzzi, Pier Paolo; Pierson, Christopher R; Finlay, Jonathan L; Leonard, Jeffrey R

    2017-05-01

    Atypical teratoid/rhabdoid tumors (AT/RT) of the central nervous system (CNS) are rare, highly malignant neoplasms that carry a poor prognosis. Even with prompt diagnosis, gross total resection and early initiation of intensive adjuvant therapy, the majority of patients will succumb within 9-12 months of diagnosis. The CPA location in children harbors lesions along a wide spectrum varying from benign to highly malignant. Imaging features of lesions within the CPA that aid the diagnostic process will help to initiate early treatment in higher-grade lesions. We report three cases, in very young children, all with cranial nerve deficits, who displayed CPA lesions with restricted diffusion on diffusion-weighted imaging (DWI) with pathology confirming AT/RT. We propose that in young children with a CPA tumor diffusion-weighted imaging should be routinely evaluated to aid in prompt management. In addition, the diagnosis of AT/RT should be highly suggestive in infants presenting with cranial nerve findings as well as DWI restricted diffusion within the CPA.

  7. Adaptive feature enhancement for mammographic images with wavelet multiresolution analysis

    NASA Astrophysics Data System (ADS)

    Chen, Lulin; Chen, Chang W.; Parker, Kevin J.

    1997-10-01

    A novel and computationally efficient approach to an adaptive mammographic image feature enhancement using wavelet-based multiresolution analysis is presented. On wavelet decomposition applied to a given mammographic image, we integrate the information of the tree-structured zero crossings of wavelet coefficients and the information of the low-pass-filtered subimage to enhance the desired image features. A discrete wavelet transform with pyramidal structure is employed to speedup the computation for wavelet decomposition and reconstruction. The spatiofrequency localization property of the wavelet transform is exploited based on the spatial coherence of image and the principle of human psycho-visual mechanism. Preliminary results show that the proposed approach is able to adaptively enhance local edge features, suppress noise, and improve global visualization of mammographic image features. This wavelet- based multiresolution analysis is therefore promising for computerized mass screening of mammograms.

  8. Cell nuclear features for classification from fluorescence images

    NASA Astrophysics Data System (ADS)

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

    2000-04-01

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

  9. 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.

  10. 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.

  11. Featured Image: Active Cryovolcanism on Europa?

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2017-05-01

    Nighttime thermal image from the Galileo Photopolarimeter-Radiometer, revealing a thermal anomaly around the region where the plumes were observed. [Sparks et al. 2017]This image shows a 1320 900 km, high-resolution Galileo/Voyager USGS map of the surface of Europa, one of Jupiters moons. In March 2014, observations of Europa revealed a plume on its icy surface coming from somewhere within the green ellipse. In February 2016, another plume was observed, this time originating from somewhere within the cyan ellipse. In addition, a nighttime thermal image from the Galileo Photopolarimeter-Radiometer has revealed a thermal anomaly a region of unusually high temperature near the same location. In a recent study led by William Sparks (Space Telescope Science Institute), a team of scientists presents these observations and argues that they provide mounting evidence of active water-vapor venting from ongoing cryovolcanism beneath Europas icy surface. If this is true, then Europas surface is active and provides access to the liquid water at depth boosting the case for Europas potential habitability and certainly making for an interesting target point for future spacecraft exploration of this moon. For more information, check out the paper below!CitationW. B. Sparks et al 2017 ApJL 839 L18. doi:10.3847/2041-8213/aa67f8

  12. 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.

  13. Featured Image: The Q Continuum Simulation

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2015-10-01

    Each frame in this image (click for the full view!) represents a different stage in the simulated evolution of our universe, ending at present day in the rightmost panel. In a recently-published paper, Katrin Heitmann (Argonne National Laboratory) and collaborators reveal the results from and challenges inherent in the largest cosmological simulation currently available: the Q Continuum simulation. Evolving a volume of (1300 Mpc)3, this massive N-body simulation tracks over half a trillion particles as they clump together as a result of their mutual gravity, imitating the evolution of our universe over the last 13.8 billion years. Cosmological simulations such as this one are important for understanding observations, testing analysis pipelines, investigating the capabilities of future observing missions, and much more. For more information and the original image (as well as several other awesome images!), see the paper below.Citation:Katrin Heitmann et al 2015 ApJS 219 34. doi:10.1088/0067-0049/219/2/34

  14. Featured Image: A Filament Forms and Erupts

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2017-06-01

    This dynamic image of active region NOAA 12241 was captured by the Solar Dynamics Observatorys Atmospheric Imaging Assembly in December 2014. Observations of this region from a number of observatories and instruments recently presented by Jincheng Wang (University of Chinese Academy of Sciences) and collaborators reveal details about the formation and eruption of a long solar filament. Wang and collaborators show that the right part of the filament formed by magnetic reconnection between two bundles of magnetic field lines, while the left part formed as a result of shearing motion. When these two parts interacted, the filament erupted. You can read more about the teams results in the article linked below. Also, check out this awesome video of the filament formation and eruption, again by SDO/AIA:http://cdn.iopscience.com/images/0004-637X/839/2/128/Full/apjaa6bf3f1_video.mp4CitationJincheng Wang et al 2017 ApJ 839 128. doi:10.3847/1538-4357/aa6bf3

  15. 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.

  16. 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

  17. Osteosarcoma of pelvic bones: imaging features.

    PubMed

    Park, Se Kyoung; Lee, In Sook; Cho, Kil Ho; Lee, Young Hwan; Yi, Jae Hyuck; Choi, Kyung Un

    The metaphyseal locations of tubular bones with osteoid mineralization in young patients are important diagnostic radiologic features of osteosarcoma. The pelvic bones are an unusual location of osteosarcoma. Although osteosarcoma occurring in pelvic bones is not common, the osteoid matrix may be a critical finding for differentiating osteosarcoma from other common pelvic bone tumors. Therefore, the possibility of osteosarcoma in pelvic bones may be considered in the presence of osteoid matrix even in the old age group. Copyright © 2016. Published by Elsevier Inc.

  18. 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.

  19. 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.

  20. Featured Image: Waves in a Coronal Fan

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2017-09-01

    The inset in this Solar Dynamics Observatory image shows a close-up view of a stunning coronal fan extending above the Suns atmosphere. These sweeping loops were observed on 7 March 2012 by a number of observatories, revealing the first known evidence of standing slow magnetoacoustic waves in cool coronal fan loops. The oscillations of the loops, studied in a recent article led by Vaibhav Pant (Indian Institute of Astrophysics), were triggered by blast waves that were generated by X-class flares from the distant active region AR 11429 (marked withthe yellow box at left). The overplotted X-ray curve in the top right corner of the image (click for the full view) shows the evolution of the flares that perturbed the footpoints of the loops. You can check out the video of the action below, and follow the link to the original article to read more about what these oscillations tell us about the Suns activity. CitationV. Pant et al 2017 ApJL 847 L5. doi:10.3847/2041-8213/aa880f

  1. 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

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

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2015-12-01

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

  3. Atypical femoral fracture after long-term alendronate treatment: report of a case evidenced with magnetic resonance imaging.

    PubMed

    Kao, Chih-Ming; Huang, Peng-Ju; Chen, Chung-Hwan; Chen, Shu-Jung; Cheng, Yuh-Min

    2012-10-01

    Postmenopausal osteoporosis is commonly treated with alendronate, one of the bisphosphonates used for the prevention and treatment of osteoporotic fractures. However, the correlation between atypical femoral fractures and long-term bisphosphonate therapy has not been clearly identified. We report here the case of a 69-year-old woman with postmenopausal osteoporosis who presented with an atypical femoral subtrochanteric fracture on magnetic resonance imaging (MRI) confirmation after having received alendronate therapy for about 3 years. The fracture united after refixation and after administration of alendronate was stopped. Several published reports were reviewed, and some clinical characteristics of this atraumatic fracture were revealed, including the clinical symptoms of thigh pain, stress reaction or stress fracture, and transverse fracture with unicortical beak in an area of cortical hypertrophy. In addition to a regular radiographic survey, MRI, which may provide early information, and bone biopsy for pathologic analysis may be used as tools for early detection and final diagnosis. Once an insufficiency fracture is suspected or proved to be related to bisphosphonate, the withholding of bisphosphonate should be highly recommended to enhance fracture healing. Prophylactic fixation should be considered if fracture healing is not good or if the patient cannot tolerate protection of weight-bearing. Copyright © 2012. Published by Elsevier B.V.

  4. Magnetic resonance imaging arterial-spin-labelling perfusion alterations in childhood migraine with atypical aura: a case-control study.

    PubMed

    Boulouis, Grégoire; Shotar, Eimad; Dangouloff-Ros, Volodia; Grévent, David; Calmon, Raphaël; Brunelle, Francis; Naggara, Olivier; Kossorotoff, Manoelle; Boddaert, Nathalie

    2016-09-01

    Atypical migraine with aura can be challenging to diagnose. Arterial-spin-labelling (ASL) is able to non-invasively quantify brain perfusion. Our aim was to report cerebral blood flow (CBF) alterations using ASL, at the acute phase of atypical migraine with aura in children. Paediatric patients were retrospectively included if (1) referred for acute neurological deficit(s), (2) underwent brain magnetic resonance imaging (MRI) at presentation with ASL sequence, and (3) had subsequent diagnosis of migraine with aura. Neurological symptom-free controls were matched for age. Twenty-eight regions of interest (ROIs) were drawn on CBF maps for each participant/control. Ten patients were included (median age 13y, range 8-16y). Eight of 10 had multiple aura symptoms during the episode. For every patient, CBF was decreased in a brain region consistent with symptoms when MRI was performed less than 14 hours after onset (n=7 patients) and increased if the MRI was performed 17 hours or more after (n=4 MRIs). MRI-ASL appears to be a promising tool for the diagnostic workup and differentials exclusion in paediatric migraine with aura. Constant and time-consistent non-territorial CBF modifications were found in our sample providing additional insight to migraine with aura pathophysiology. The authors encourage implementing this sequence at the acute phase of unexplained paediatric neurological deficits, with or without accompanying headache. © 2016 Mac Keith Press.

  5. 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.

  6. Automatic seamless image mosaic method based on SIFT features

    NASA Astrophysics Data System (ADS)

    Liu, Meiying; Wen, Desheng

    2017-02-01

    An automatic seamless image mosaic method based on SIFT features is proposed. First a scale-invariant feature extracting algorithm SIFT is used for feature extraction and matching, which gains sub-pixel precision for features extraction. Then, the transforming matrix H is computed with improved PROSAC algorithm , compared with RANSAC algorithm, the calculate efficiency is advanced, and the number of the inliers are more. Then the transforming matrix H is purify with LM algorithm. And finally image mosaic is completed with smoothing algorithm. The method implements automatically and avoids the disadvantages of traditional image mosaic method under different scale and illumination conditions. Experimental results show the image mosaic effect is wonderful and the algorithm is stable very much. It is high valuable in practice.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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

  12. 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

  13. Featured Image: Star Clusters in M51

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2016-06-01

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

  14. 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.

  15. Image processing tool for automatic feature recognition and quantification

    DOEpatents

    Chen, Xing; Stoddard, Ryan J.

    2017-05-02

    A system for defining structures within an image is described. The system includes reading of an input file, preprocessing the input file while preserving metadata such as scale information and then detecting features of the input file. In one version the detection first uses an edge detector followed by identification of features using a Hough transform. The output of the process is identified elements within the image.

  16. 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

  17. Anatomic, histopathologic, and echocardiographic features in a dog with an atypical pulmonary valve stenosis with a fibrous band of tissue and a patent ductus arteriosus.

    PubMed

    Yoon, Hakyoung; Kim, Jaehwan; Nahm, Sang-Soep; Eom, Kidong

    2017-07-11

    Congenital pulmonary valve stenosis and patent ductus arteriosus are common congenital heart defects in dogs. However, concurrence of atypical pulmonary valve stenosis and patent ductus arteriosus is uncommon. This report describes the anatomic, histopathologic, and echocardiographic features in a dog with concomitant pulmonary valve stenosis and patent ductus arteriosus with atypical pulmonary valve dysplasia that included a fibrous band of tissue. A 1.5-year-old intact female Chihuahua dog weighing 3.3 kg presented with a continuous grade VI cardiac murmur, poor exercise tolerance, and an intermittent cough. Echocardiography indicated pulmonary valve stenosis, a thickened dysplastic valve without annular hypoplasia, and a type IIA patent ductus arteriosus. The pulmonary valve was thick line-shaped in systole and dome-shaped towards the right ventricular outflow tract in diastole. The dog suffered a fatal cardiac arrest during an attempted balloon pulmonary valvuloplasty. Necropsy revealed pulmonary valve dysplasia, commissural fusion, and incomplete opening and closing of the pulmonary valve because of a fibrous band of tissue causing adhesion between the right ventricular outflow tract and the dysplastic intermediate cusp of the valve. A fibrous band of tissue between the right ventricular outflow track and the pulmonary valve should be considered as a cause of pulmonary valve stenosis. Pulmonary valve stenosis and patent ductus arteriosus can have conflicting effects on diastolic and systolic dysfunction, respectively. Therefore, beta-blockers should always be used carefully, particularly in patients with a heart defect where there is concern about left ventricular systolic function.

  18. 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).

  19. 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.

  20. 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.

  1. 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.

  2. 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

  3. 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

  4. 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

  5. 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

  6. Featured Image: The Cosmic Velocity Web

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2017-09-01

    You may have heard of the cosmic web, a network of filaments, clusters and voids that describes the three-dimensional distribution of matter in our universe. But have you ever considered the idea of a cosmic velocity web? In a new study led by Daniel Pomarde (IRFU CEA-Saclay, France), a team of scientists has built a detailed 3D view of the flows in our universe, showing in particular motions along filaments and in collapsing knots. In the image above (click for the full view), surfaces of knots (red) are embedded within surfaces of filaments (grey). The rainbow lines show the flow motion, revealing acceleration (redder tones) toward knots and retardation (bluer tones) beyond them. You can learn more about Pomarde and collaborators work and see their unusual and intriguing visualizationsin the video they produced, below. Check out the original paper for more information.CitationDaniel Pomarde et al 2017 ApJ 845 55. doi:10.3847/1538-4357/aa7f78

  7. 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

  8. 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

  9. 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.

  10. Radiometric normalization with multi-image pseudo-invariant features

    NASA Astrophysics Data System (ADS)

    Barazzetti, Luigi; Gianinetto, Marco; Scaioni, Marco

    2016-08-01

    Radiometric image normalization is one of the basic pre-processing methods used in satellite time series analysis. This paper presents a new multi-image approach able to estimate the parameters of relative radiometric normalization through a multiple and simultaneous regression with a dataset of a generic number of images. The method was developed to overcome the typical drawbacks of standard one-to-one techniques, where image pairs are independently processed. The proposed solution is based on multi-image pseudo-invariant features incorporated into a unique regression solved via Least Squares. Results for both simulated and real data are presented and discussed.

  11. 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

  12. 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

  13. 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

  14. 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.

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

    DTIC Science & Technology

    2008-03-01

    Pulse- echo image formation using nonquadratic regularization with speckle -based images,” M.S. thesis, Univ. of Illinois Urbana-Champaign, 2005. [8] D...imaging ideas developed in this project, as a foundation for feature-based tracking and ATR based on multi-sensor data. 4 PROFESSIONAL ACTIVITIES AND...directional interference. Such passive multistatic radar systems, such as Lockheed Martin’s Silent Sentry, have been developed to detect and track

  16. Imaging features of Burkitt lymphoma in pediatric patients

    PubMed Central

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

    2016-01-01

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

  17. Imaging features of Burkitt lymphoma in pediatric patients.

    PubMed

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

    2016-01-01

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

  18. 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.

  19. Face recognition with multi-resolution spectral feature images.

    PubMed

    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.

  20. 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.

  1. 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

  2. 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.

  3. 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.

  4. 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

  5. Nuclei-Based Features for Uterine Cervical Cancer Histology Image Analysis With Fusion-Based Classification.

    PubMed

    Guo, Peng; Banerjee, Koyel; Joe Stanley, R; Long, Rodney; Antani, Sameer; Thoma, George; Zuna, Rosemary; Frazier, Shelliane R; Moss, Randy H; Stoecker, William V

    2016-11-01

    Cervical cancer, which has been affecting women worldwide as the second most common cancer, can be cured if detected early and treated well. Routinely, expert pathologists visually examine histology slides for cervix tissue abnormality assessment. In previous research, we investigated an automated, localized, fusion-based approach for classifying squamous epithelium into Normal, CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia (CIN) based on image analysis of 61 digitized histology images. This paper introduces novel acellular and atypical cell concentration features computed from vertical segment partitions of the epithelium region within digitized histology images to quantize the relative increase in nuclei numbers as the CIN grade increases. Based on the CIN grade assessments from two expert pathologists, image-based epithelium classification is investigated with voting fusion of vertical segments using support vector machine and linear discriminant analysis approaches. Leave-one-out is used for the training and testing for CIN classification, achieving an exact grade labeling accuracy as high as 88.5%.

  6. 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.

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

    PubMed Central

    2013-01-01

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

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

    PubMed

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

    2013-12-01

    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. 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. 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, the reproducible feature sets

  9. 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

  10. 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.

  11. 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.

  12. [Immunofluorescence assay with Crithidia luciliae for the detection of anti-DNA antibodies. Atypical images and their relationship with Chagas' disease and leishmaniasis].

    PubMed

    Griemberg, Gloria; Ferrarotti, Nidia F; Svibel, Graciela; Ravelli, Maria R; Taranto, Nestor J; Malchiodi, Emilio L; Pizzimenti, Maria C

    2006-01-01

    Anti-native DNA antibodies can be detected by indirect immunofluorescence assay with Crithidia luciliae, displaying an annular image due to a kinetoplast containing double stranded DNA. Other structures such as membrane, flagellum and basal corpuscle can be stained as well, showing what is called atypical fluorescent images. As C. luciliae belongs to the Trypanosomatidae family, which include the human pathogens Trypanosoma cruzi and Leishmania spp., it was considered that these atypical images could be caused by cross-reactions. Serological studies for Chagas' disease were performed in 105 serum samples displaying atypical images. Sixty four percent of the samples from non endemic and 78.3% from endemic areas for Chagas' disease showed fluorescence in both, membrane and flagellum (joint image). Fifty samples from normal blood donors and 57 samples from patients with conective tissue diseases were tested with C. luciliae. None of them presented the joint image except for two patients with lupus who were also chagasic. In addition, 54 samples from chagasic patients were studied and all of them presented the joint image. We also studied 46 samples from patients with leishmaniasis from whom 28 were coinfected with T. cruzi. The joint image was observed in 88.0% of the samples with leishmaniasis and in 89.3% of the co-infected samples. The results suggest that C. luciliae could be used as an economical, and of low risk, alternative substrate for the serological diagnosis of Chagas' disease, even though it does not discriminate for Leishmania spp. infection. This study also suggests that whenever atypical images are observed in C. luciliae during the search for anti-DNA antibodies, it would be convenient to submit the patient to clinical and serological tests for the diagnosis of leishmaniosis and Chagas' disease.

  13. 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.

  14. 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.

  15. 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

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. Combinational feature optimization for classification of lung tissue images

    NASA Astrophysics Data System (ADS)

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

    2010-03-01

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

  1. 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.

  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. 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.

  4. Optimization of wavelet decomposition for image compression and feature preservation.

    PubMed

    Lo, Shih-Chung B; Li, Huai; Freedman, Matthew T

    2003-09-01

    A neural-network-based framework has been developed to search for an optimal wavelet kernel that can be used for a specific image processing task. In this paper, a linear convolution neural network was employed to seek a wavelet that minimizes errors and maximizes compression efficiency for an image or a defined image pattern such as microcalcifications in mammograms and bone in computed tomography (CT) head images. We have used this method to evaluate the performance of tap-4 wavelets on mammograms, CTs, magnetic resonance images, and Lena images. We found that the Daubechies wavelet or those wavelets with similar filtering characteristics can produce the highest compression efficiency with the smallest mean-square-error for many image patterns including general image textures as well as microcalcifications in digital mammograms. However, the Haar wavelet produces the best results on sharp edges and low-noise smooth areas. We also found that a special wavelet whose low-pass filter coefficients are 0.32252136, 0.85258927, 1.38458542, and -0.14548269) produces the best preservation outcomes in all tested microcalcification features including the peak signal-to-noise ratio, the contrast and the figure of merit in the wavelet lossy compression scheme. Having analyzed the spectrum of the wavelet filters, we can find the compression outcomes and feature preservation characteristics as a function of wavelets. This newly developed optimization approach can be generalized to other image analysis applications where a wavelet decomposition is employed.

  5. Improved image retrieval based on fuzzy colour feature vector

    NASA Astrophysics Data System (ADS)

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

    2013-03-01

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

  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. An image-processing methodology for extracting bloodstain pattern features.

    PubMed

    Arthur, Ravishka M; Humburg, Philomena J; Hoogenboom, Jerry; Baiker, Martin; Taylor, Michael C; de Bruin, Karla G

    2017-08-01

    There is a growing trend in forensic science to develop methods to make forensic pattern comparison tasks more objective. This has generally involved the application of suitable image-processing methods to provide numerical data for identification or comparison. This paper outlines a unique image-processing methodology that can be utilised by analysts to generate reliable pattern data that will assist them in forming objective conclusions about a pattern. A range of features were defined and extracted from a laboratory-generated impact spatter pattern. These features were based in part on bloodstain properties commonly used in the analysis of spatter bloodstain patterns. The values of these features were consistent with properties reported qualitatively for such patterns. The image-processing method developed shows considerable promise as a way to establish measurable discriminating pattern criteria that are lacking in current bloodstain pattern taxonomies. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. Feature detection in satellite images using neural network technology

    NASA Technical Reports Server (NTRS)

    Augusteijn, Marijke F.; Dimalanta, Arturo S.

    1992-01-01

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

  9. Research on Forest Flame Recognition Algorithm Based on Image Feature

    NASA Astrophysics Data System (ADS)

    Wang, Z.; Liu, P.; Cui, T.

    2017-09-01

    In recent years, fire recognition based on image features has become a hotspot in fire monitoring. However, due to the complexity of forest environment, the accuracy of forest fireworks recognition based on image features is low. Based on this, this paper proposes a feature extraction algorithm based on YCrCb color space and K-means clustering. Firstly, the paper prepares and analyzes the color characteristics of a large number of forest fire image samples. Using the K-means clustering algorithm, the forest flame model is obtained by comparing the two commonly used color spaces, and the suspected flame area is discriminated and extracted. The experimental results show that the extraction accuracy of flame area based on YCrCb color model is higher than that of HSI color model, which can be applied in different scene forest fire identification, and it is feasible in practice.

  10. Common feature discriminant analysis for matching infrared face images to optical face images.

    PubMed

    Li, Zhifeng; Gong, Dihong; Qiao, Yu; Tao, Dacheng

    2014-06-01

    In biometrics research and industry, it is critical yet a challenge to match infrared face images to optical face images. The major difficulty lies in the fact that a great discrepancy exists between the infrared face image and corresponding optical face image because they are captured by different devices (optical imaging device and infrared imaging device). This paper presents a new approach called common feature discriminant analysis to reduce this great discrepancy and improve optical-infrared face recognition performance. In this approach, a new learning-based face descriptor is first proposed to extract the common features from heterogeneous face images (infrared face images and optical face images), and an effective matching method is then applied to the resulting features to obtain the final decision. Extensive experiments are conducted on two large and challenging optical-infrared face data sets to show the superiority of our approach over the state-of-the-art.

  11. 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.

  12. 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

  13. Atypical adenomatous hyperplasia —

    Cancer.gov

    Focal and diffuse lesions involving alveoli and terminal bronchioles and consisting of relatively uniform atypical cuboidal to columnar cells with dense chromatin. Degrees of cellular hypertrophy and hyperchromasia are variable. Cellular and nuclear atypia are the distinctive features as compared with hyperplasia. Their relevance to human AAH and mouse adenomas remains to be determined.

  14. 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.

  15. 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.

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

    PubMed

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

    2015-09-01

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

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

    PubMed Central

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

    2015-01-01

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

  18. 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.

  19. Feature analysis for detecting people from remotely sensed images

    NASA Astrophysics Data System (ADS)

    Sirmacek, Beril; Reinartz, Peter

    2013-01-01

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

  20. 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.

  1. Scene classification of infrared images based on texture feature

    NASA Astrophysics Data System (ADS)

    Zhang, Xiao; Bai, Tingzhu; Shang, Fei

    2008-12-01

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

  2. 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."

  3. 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.

  4. Diagnostic Approach to Atypical Parkinsonian Syndromes

    PubMed Central

    McFarland, Nikolaus R.

    2016-01-01

    ABSTRACT Purpose of Review: Although increasingly recognized, atypical parkinsonian syndromes remain challenging to diagnose and are underrecognized due to overlap with other parkinsonisms. This article provides a diagnostic approach to atypical parkinsonian syndromes, including progressive supranuclear palsy (PSP), multiple system atrophy (MSA), corticobasal degeneration (CBD), and dementia with Lewy bodies. The goal of this review is to aid the clinician in recognizing key clinical and pathologic features and to raise awareness of recent advances in diagnostics and treatment. Recent Findings: Diagnostic criteria for atypical parkinsonian syndromes are evolving to encompass increasingly recognized heterogeneity in the presentation of these disorders and information gleamed from clinicopathologic correlations. PSP and CBD in particular now share similar pathologic clinical features and include a number of phenotypic variants. Pathologic diagnoses are increasingly used in clinical practice, and there is frequent reference now by clinicians to tauopathies, including PSP and CBD, and the synucleinopathies, which include MSA and dementia with Lewy bodies (as well as Parkinson disease). Research into biomarkers, including both tissue and imaging modalities and genetics, has the potential to increase disease recognition and make earlier diagnosis and treatment possible. Although novel therapeutics are being studied for atypical parkinsonian syndromes such as PSP, no new breakthrough interventions have emerged for the treatment of PSP, CBD, and MSA. Current therapeutic management for these disorders frequently uses a multidisciplinary team approach. Summary: The approach to atypical parkinsonian syndromes requires recognition of a constellation of overlapping but distinct clinical features that help with identifying and distinguishing them from Parkinson disease and other similar disorders. PMID:27495201

  5. 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.

  6. Melorheostosis: Two atypical cases

    PubMed Central

    Sureka, Binit; Mittal, Mahesh Kumar; Udhaya, KK; Sinha, Mukul; Mittal, Aliza; Thukral, Brij Bhushan

    2014-01-01

    Melorheostosis is an uncommon mesenchymal dysplasia that rarely affects the axial skeleton. We describe two atypical cases of melorheostosis with classical imaging findings – the first one involving the cervico-dorsal spine with encroachment of left vertebral artery canal causing attenuation of the left vertebral artery and the second one of mixed sclerosing bony dysplasia (monomelic involvement coexisting with osteopoikilosis). PMID:25024532

  7. Melorheostosis: Two atypical cases.

    PubMed

    Sureka, Binit; Mittal, Mahesh Kumar; Udhaya, Kk; Sinha, Mukul; Mittal, Aliza; Thukral, Brij Bhushan

    2014-04-01

    Melorheostosis is an uncommon mesenchymal dysplasia that rarely affects the axial skeleton. We describe two atypical cases of melorheostosis with classical imaging findings - the first one involving the cervico-dorsal spine with encroachment of left vertebral artery canal causing attenuation of the left vertebral artery and the second one of mixed sclerosing bony dysplasia (monomelic involvement coexisting with osteopoikilosis).

  8. Computational optical sensing and imaging: introduction to feature issue.

    PubMed

    Gerwe, David R; Harvey, Andrew; Gehm, Michael E

    2013-04-01

    The 2012 Computational Optical Sensing and Imaging (COSI) conference of the Optical Society of America was one of six colocated meetings composing the Imaging and Applied Optics Congress held in Monterey, California, 24-28 June. COSI, together with the Imaging Systems and Applications, Optical Sensors, Applied Industrial Optics, and Optical Remote Sensing of the Environment conferences, brought together a diverse group of scientists and engineers sharing a common interest in measuring and processing of information carried by optical fields. This special feature includes several papers based on presentations given at the 2012 COSI conference as well as independent contributions, which together highlight several important trends.

  9. Adaptive color feature extraction based on image color distributions.

    PubMed

    Chen, Wei-Ta; Liu, Wei-Chuan; Chen, Ming-Syan

    2010-08-01

    This paper proposes an adaptive color feature extraction scheme by considering the color distribution of an image. Based on the binary quaternion-moment-preserving (BQMP) thresholding technique, the proposed extraction methods, fixed cardinality (FC) and variable cardinality (VC), are able to extract color features by preserving the color distribution of an image up to the third moment and to substantially reduce the distortion incurred in the extraction process. In addition to utilizing the earth mover's distance (EMD) as the distance measure of our color features, we also devise an efficient and effective distance measure, comparing histograms by clustering (CHIC). Moreover, the efficient implementation of our extraction methods is explored. With slight modification of the BQMP algorithm, our extraction methods are equipped with the capability of exploiting the concurrent property of hardware implementation. The experimental results show that our hardware implementation can achieve approximately a second order of magnitude improvement over the software implementation. It is noted that minimizing the distortion incurred in the extraction process can enhance the accuracy of the subsequent various image applications, and we evaluate the meaningfulness of the new extraction methods by the application to content-based image retrieval (CBIR). Our experimental results show that the proposed extraction methods can enhance the average retrieval precision rate by a factor of 25% over that of a traditional color feature extraction method.

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

    USDA-ARS?s Scientific Manuscript database

    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...

  11. MR Imaging Features of Obturator Internus Bursa of the Hip

    PubMed Central

    Lee, Sun Wha; Kim, Jong Oh

    2008-01-01

    The authors report two cases with distension of the obturator internus bursa identified on MR images, and describe the location and characteristic features of obturator internus bursitis; the "boomerang"-shaped fluid distension between the obturator internus tendon and the posterior grooved surface of the ischium. PMID:18682677

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

    USDA-ARS?s Scientific Manuscript database

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

  13. 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

  14. The Effect of Image Enhancement Methods during Feature Detection and Matching of Thermal Images

    NASA Astrophysics Data System (ADS)

    Akcay, O.; Avsar, E. O.

    2017-05-01

    A successful image matching is essential to provide an automatic photogrammetric process accurately. Feature detection, extraction and matching algorithms have performed on the high resolution images perfectly. However, images of cameras, which are equipped with low-resolution thermal sensors are problematic with the current algorithms. In this paper, some digital image processing techniques were applied to the low-resolution images taken with Optris PI 450 382 x 288 pixel optical resolution lightweight thermal camera to increase extraction and matching performance. Image enhancement methods that adjust low quality digital thermal images, were used to produce more suitable images for detection and extraction. Three main digital image process techniques: histogram equalization, high pass and low pass filters were considered to increase the signal-to-noise ratio, sharpen image, remove noise, respectively. Later on, the pre-processed images were evaluated using current image detection and feature extraction methods Maximally Stable Extremal Regions (MSER) and Speeded Up Robust Features (SURF) algorithms. Obtained results showed that some enhancement methods increased number of extracted features and decreased blunder errors during image matching. Consequently, the effects of different pre-process techniques were compared in the paper.

  15. Multi-modal image registration using structural features.

    PubMed

    Kasiri, Keyvan; Clausi, David A; Fieguth, Paul

    2014-01-01

    Multi-modal image registration has been a challenging task in medical images because of the complex intensity relationship between images to be aligned. Registration methods often rely on the statistical intensity relationship between the images which suffers from problems such as statistical insufficiency. The proposed registration method works based on extracting structural features by utilizing the complex phase and gradient-based information. By employing structural relationships between different modalities instead of complex similarity measures, the multi-modal registration problem is converted into a mono-modal one. Therefore, conventional mono-modal similarity measures can be utilized to evaluate the registration results. This new registration paradigm has been tested on magnetic resonance (MR) brain images of different modes. The method has been evaluated based on target registration error (TRE) to determine alignment accuracy. Quantitative results demonstrate that the proposed method is capable of achieving comparable registration accuracy compared to the conventional mutual information.

  16. Tunable filters for multispectral imaging of aeronomical features

    NASA Astrophysics Data System (ADS)

    Goenka, C.; Semeter, J. L.; Noto, J.; Dahlgren, H.; Marshall, R.; Baumgardner, J.; Riccobono, J.; Migliozzi, M.

    2013-10-01

    Multispectral imaging of optical emissions in the Earth's upper atmosphere unravels vital information about dynamic phenomena in the Earth-space environment. Wavelength tunable filters allow us to accomplish this without using filter wheels or multiple imaging setups, but with identifiable caveats and trade-offs. We evaluate one such filter, a liquid crystal Fabry-Perot etalon, as a potential candidate for the next generation of imagers for aeronomy. The tunability of such a filter can be exploited in imaging features such as the 6300-6364 Å oxygen emission doublet, or studying the rotational temperature of N2+ in the 4200-4300 Å range, observations which typically require multiple instruments. We further discuss the use of this filter in an optical instrument, called the Liquid Crystal Hyperspectral Imager (LiCHI), which will be developed to make simultaneous measurements in various wavelength ranges.

  17. 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.

  18. 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.

  19. 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.

  20. Characterisation of Feature Points in Eye Fundus Images

    NASA Astrophysics Data System (ADS)

    Calvo, D.; Ortega, M.; Penedo, M. G.; Rouco, J.

    The retinal vessel tree adds decisive knowledge in the diagnosis of numerous opthalmologic pathologies such as hypertension or diabetes. One of the problems in the analysis of the retinal vessel tree is the lack of information in terms of vessels depth as the image acquisition usually leads to a 2D image. This situation provokes a scenario where two different vessels coinciding in a point could be interpreted as a vessel forking into a bifurcation. That is why, for traking and labelling the retinal vascular tree, bifurcations and crossovers of vessels are considered feature points. In this work a novel method for these retinal vessel tree feature points detection and classification is introduced. The method applies image techniques such as filters or thinning to obtain the adequate structure to detect the points and sets a classification of these points studying its environment. The methodology is tested using a standard database and the results show high classification capabilities.

  1. Image Features As Virtual Beacons For Local Navigation

    NASA Astrophysics Data System (ADS)

    Engel, Antonie J.

    1989-03-01

    A technique for dynamic position correction using image features as virtual beacons is described. An algorithm which acquires new features, computes robot position correction vectors from tracked features, and maintains feature reliability statistics is detailed. The algorithm minimizes the use of matching to reduce computational expense and increase robustness. The principal inputs to the algorithm are the relative bearings observed between feature pairs. Unlike stereo-vision techniques it does not compute explicit feature range estimates. Unlike the bulk of vision based navigation methods, an accurate position estimate results from the integration of a large number of correction vectors derived from the low level analysis of many images. A control architecture for an autonomous mobile robot which makes use of this positioning technique is discussed. The general navigation problem of positioning, model building, path finding, and path execution is decomposed into local and global navigation. Local navigation is independent of high level representations, it is concerned with the immediately perceivable environment and deals with the bulk of the real-time constraints. Methods for coupling local and global navigation are explored. Simulation results showing the behavior of such a control system are presented. The motivation behind this research is the belief that a substantial subset of the navigation problem can be solved using only information obtained during early vision processing. This technique is expected to be more computationally tractable than methods based on optical flow field determination and more accurate than landmark based navigation methods.

  2. Pituitary apoplexy: an update on clinical and imaging features.

    PubMed

    Boellis, Alessandro; di Napoli, Alberto; Romano, Andrea; Bozzao, Alessandro

    2014-12-01

    Pituitary apoplexy (PA) is a rare and potentially fatal clinical condition presenting acute headache, vomiting, visual impairment, ophthalmoplegia, altered mental state and possible panhypopituitarism. It mostly occurs in patients with haemorrhagic infarction of the pituitary gland due to a pre-existing macroadenoma. Although there are pathological and physiological conditions that may share similar imaging characteristics, both clinical and imaging features can guide the radiologist towards the correct diagnosis, especially using magnetic resonance imaging (MRI). In this review, we will describe the main clinical and epidemiological features of PA, illustrating CT and MRI findings and discussing the role of imaging in the differential diagnosis, prognosis and follow-up. Teaching points • Headache, ophtalmoplegia and visual impairment are frequent symptoms of pituitary apoplexy. • CT is often the first imaging tool in PA, showing areas of hyperdensity within the sellar region. • MRI could confirm haemorrhage within the pituitary gland and compression on the optic chiasm. • Frequent simulating conditions are aneurysms, Rathke cleft cysts, craniopharingioma and mucocele. • The role of imaging is still debated and needs more studies.

  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 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

  5. 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.

  6. 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.

  7. 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.

  8. Central nervous system lymphoma: magnetic resonance imaging features at presentation.

    PubMed

    Schwingel, Ricardo; Reis, Fabiano; Zanardi, Veronica A; Queiroz, Luciano S; França, Marcondes C

    2012-02-01

    This paper aimed at studying presentations of the central nervous system (CNS) lymphoma using structural images obtained by magnetic resonance imaging (MRI). The MRI features at presentation of 15 patients diagnosed with CNS lymphoma in a university hospital, between January 1999 and March 2011, were analyzed by frequency and cross tabulation. All patients had supratentorial lesions; and four had infra- and supratentorial lesions. The signal intensity on T1 and T2 weighted images was predominantly hypo- or isointense. In the T2 weighted images, single lesions were associated with a hypointense signal component. Six patients presented necrosis, all of them showed perilesional abnormal white matter, nine had meningeal involvement, and five had subependymal spread. Subependymal spread and meningeal involvement tended to occur in younger patients. Presentations of lymphoma are very pleomorphic, but some of them should point to this diagnostic possibility.

  9. 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

  10. The fuzzy Hough transform-feature extraction in medical images.

    PubMed

    Philip, K P; Dove, E L; McPherson, D D; Gotteiner, N L; Stanford, W; Chandran, K B

    1994-01-01

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

  11. 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.

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

    PubMed

    Subakan, Ozlem; Jian, Bing; Vemuri, Baba C; Vallejos, C Eduardo

    2007-10-14

    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.

  13. 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

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

    PubMed

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

    2012-06-01

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

  15. 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

  16. 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.

  17. A comparison study of image features between FFDM and film mammogram images.

    PubMed

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

    2012-07-01

    This work is to provide a direct, quantitative comparison of image features measured by film and full-field digital mammography (FFDM). The purpose is to investigate whether there is any systematic difference between film and FFDM in terms of quantitative image features and their influence on the performance of a computer-aided diagnosis (CAD) system. The authors make use of a set of matched film-FFDM image pairs acquired from cadaver breast specimens with simulated microcalcifications consisting of bone and teeth fragments using both a GE digital mammography system and a screen-film system. To quantify the image features, the authors consider a set of 12 textural features of lesion regions and six image features of individual microcalcifications (MCs). The authors first conduct a direct comparison on these quantitative features extracted from film and FFDM images. The authors then study the performance of a CAD classifier for discriminating between MCs and false positives (FPs) when the classifier is trained on images of different types (film, FFDM, or both). For all the features considered, the quantitative results show a high degree of correlation between features extracted from film and FFDM, with the correlation coefficients ranging from 0.7326 to 0.9602 for the different features. Based on a Fisher sign rank test, there was no significant difference observed between the features extracted from film and those from FFDM. For both MC detection and discrimination of FPs from MCs, FFDM had a slight but statistically significant advantage in performance; however, when the classifiers were trained on different types of images (acquired with FFDM or SFM) for discriminating MCs from FPs, there was little difference. The results indicate good agreement between film and FFDM in quantitative image features. While FFDM images provide better detection performance in MCs, FFDM and film images may be interchangeable for the purposes of training CAD algorithms, and a single

  18. 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.

  19. 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.

  20. Nearest feature line embedding approach to hyperspectral image classification

    NASA Astrophysics Data System (ADS)

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

    2012-10-01

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

  1. 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

  2. Extraocular retinoblastoma in Indian children: clinical, imaging and histopathological features

    PubMed Central

    Sethi, Sumita; Pushker, Neelam; Kashyap, Seema; Sharma, Sanjay; Mehta, Mridula; Bakhshi, Sameer; Khurana, Saurbhi; Ghose, Supriyo

    2013-01-01

    AIM To study eyes with extraocular dissemination (EORB), with the following aims: first to establish the mean lag period and to understand various reasons for delayed presentation, second to study their imaging profiles and third to analyze histopathological features of eyes enucleated after neoadjuvant chemotherapy. METHODS Prospective study of clinical and imaging features of EORBs (stage III and IV International Retinoblastoma Staging System) presenting to a tertiary eye care centre. Histopathological features of eyes enucleated after receiving neoadjuvant chemotherapy were analyzed. A pictorial illustration of the varied imaging profile of EORB was also presented. RESULTS Over a period of one year, 97 eyes were diagnosed with retinoblastoma; 32 children (36 eyes) (37.1%) had EORB. Mean age 3.6±1.9 years, 71.9% males, 71.9% unilateral, 3.1% with positive family history and 40.6% with metastasis. On imaging, there was extrascleral involvement in 22.2%, involvement of orbital part of optic nerve in 33.3%, involvement of central nervous system in 27.8% and orbital wall involvement in 2.9% eyes. On histopathological analysis of eyes enucleated after neoadjuvant chemotherapy, 25.0% had no residual viable tumour tissue and rest all tumours were poorly differentiated. CONCLUSION There are very few human malignancies where definitive treatment is started without any confirmed histopathological diagnosis and imaging plays an important role in diagnosis and appropriate staging of the disease. Chemotherapy has a variable effect on EORB, 75.0% of eyes with EORB had residual viable tumour tissue when enucleated after receiving neoadjuvant chemotherapy. PMID:23991383

  3. Extraocular retinoblastoma in Indian children: clinical, imaging and histopathological features.

    PubMed

    Sethi, Sumita; Pushker, Neelam; Kashyap, Seema; Sharma, Sanjay; Mehta, Mridula; Bakhshi, Sameer; Khurana, Saurbhi; Ghose, Supriyo

    2013-01-01

    TO STUDY EYES WITH EXTRAOCULAR DISSEMINATION (EORB), WITH THE FOLLOWING AIMS: first to establish the mean lag period and to understand various reasons for delayed presentation, second to study their imaging profiles and third to analyze histopathological features of eyes enucleated after neoadjuvant chemotherapy. Prospective study of clinical and imaging features of EORBs (stage III and IV International Retinoblastoma Staging System) presenting to a tertiary eye care centre. Histopathological features of eyes enucleated after receiving neoadjuvant chemotherapy were analyzed. A pictorial illustration of the varied imaging profile of EORB was also presented. Over a period of one year, 97 eyes were diagnosed with retinoblastoma; 32 children (36 eyes) (37.1%) had EORB. Mean age 3.6±1.9 years, 71.9% males, 71.9% unilateral, 3.1% with positive family history and 40.6% with metastasis. On imaging, there was extrascleral involvement in 22.2%, involvement of orbital part of optic nerve in 33.3%, involvement of central nervous system in 27.8% and orbital wall involvement in 2.9% eyes. On histopathological analysis of eyes enucleated after neoadjuvant chemotherapy, 25.0% had no residual viable tumour tissue and rest all tumours were poorly differentiated. There are very few human malignancies where definitive treatment is started without any confirmed histopathological diagnosis and imaging plays an important role in diagnosis and appropriate staging of the disease. Chemotherapy has a variable effect on EORB, 75.0% of eyes with EORB had residual viable tumour tissue when enucleated after receiving neoadjuvant chemotherapy.

  4. 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

  5. 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

  6. 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.

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

    PubMed

    Fei, Yin; 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.

  8. Characterization of atypical cystic renal masses with MDCT: comparison of 5-mm axial images and thin multiplanar reconstructed images.

    PubMed

    Bertolotto, Michele; Zappetti, Roberta; Cavallaro, Marco; Perrone, Rosaria; Perretti, Leonardo; Cova, Maria Assunta

    2010-09-01

    The purpose of this study was to investigate whether cystic renal masses are better characterized on thin axial and multiplanar reconstructed MDCT images than on 5-mm images. The records of 70 complex cystic renal masses in 59 patients (45 men, 14 women; mean age, 68 +/- 13 years) who underwent 64-MDCT at two medical centers were studied. Twenty-three of the masses were confirmed on the basis of the histologic findings and 47 in 2-4 years of follow-up. Images were reviewed in two sessions by two radiologists with 12 and 2 years of experience. In the first session, 5-mm axial images were analyzed, and in the second, thin axial images and multiplanar reconstructions. To assess intraobserver variability, analysis was repeated after 1 month. Statistical analysis was performed with Wilcoxon's signed rank test, receiver operating characteristic analysis, and weighted kappa statistics. Radiologists 1 and 2 detected thicker cystic walls (p < 0.001, p < 0.005) and septa (p < 0.03, p < 0.05) and fewer septa (p < 0.005, p < 0.002) on 5-mm axial images and assigned significantly different Bosniak categories than they did in analysis of the volume data (p < 0.04, p < 0.05). Variability was reduced in thin axial and multiplanar views. No significant differences were found in characterization of lesions as benign or malignant in review of 5-mm axial images and volume data sets. The areas under the receiver operating characteristic curve were 0.89 for 5-mm images and 0.96 for volume data sets for radiologist 1 and 0.87 and 0.90 for radiologist 2. Analysis of volume data sets is associated with less intraobserver and interobserver variability than review of 5-mm axial images. Wall thickness and the number and thickness of septa may differ, resulting in assignment of different Bosniak categories. Diagnostic performance in characterizing lesions as benign or malignant, however, is not statistically different for the thick and thin images.

  9. 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.

  10. 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.

  11. 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.

  12. 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

  13. 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

  14. Atypical Learning in Autism Spectrum Disorders: A Functional Magnetic Resonance Imaging Study of Transitive Inference

    PubMed Central

    Solomon, Marjorie; Ragland, J. Daniel; Niendam, Tara A.; Lesh, Tyler A.; Beck, Jonathan S.; Matter, John C.; Frank, Michael J.; Carter, Cameron S.

    2015-01-01

    Objective To investigate the neural mechanisms underlying impairments in generalizing learning shown by adolescents with autism spectrum disorder (ASD). Method Twenty-one high-functioning individuals with ASD aged 12–18 years, and 23 gender, IQ, and age-matched adolescents with typical development (TYP) completed a transitive inference (TI) task implemented using rapid event-related functional magnetic resonance imaging (fMRI). They were trained on overlapping pairs in a stimulus hierarchy of colored ovals where A>B>C>D>E>F and then tested on generalizing this training to new stimulus pairings (AF, BD, BE) in a “Big Game.” Whole-brain univariate, region of interest, and functional connectivity analyses were used. Results During training, TYP exhibited increased recruitment of the prefrontal cortex (PFC), while the group with ASD showed greater functional connectivity between the PFC and the anterior cingulate cortex (ACC). Both groups recruited the hippocampus and caudate comparably; however, functional connectivity between these regions was positively associated with TI performance for only the group with ASD. During the Big Game, TYP showed greater recruitment of the PFC, parietal cortex, and the ACC. Recruitment of these regions increased with age in the group with ASD. Conclusion During TI, TYP recruited cognitive control-related brain regions implicated in mature problem solving/reasoning including the PFC, parietal cortex, and ACC, while the group with ASD showed functional connectivity of the hippocampus and the caudate that was associated with task performance. Failure to reliably engage cognitive control-related brain regions may produce less integrated flexible learning in those with ASD unless they are provided with task support that in essence provides them with cognitive control, but this pattern may normalize with age. PMID:26506585

  15. 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.

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

    PubMed Central

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

    2013-01-01

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

  17. 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

  18. 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.

  19. 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.

  20. Enhancing microscopy images of minerals through morphological center operator-based feature extraction.

    PubMed

    Bai, Xiangzhi

    2013-02-01

    To well enhance the mineral image and image details obtained by microscopes, an effective mineral image enhancement algorithm through feature extraction using the morphological center operator is proposed in this work. First, mineral image feature extraction based on the morphological center operator is proposed and discussed. Second, the multiscale extension of the mineral image feature extraction is given by using the multiscale structuring elements. Third, the important mineral image features at multiscales of image are extracted and used to construct the final mineral features for mineral image enhancement. Finally, the original mineral image is well enhanced through importing the extracted final mineral image features into the original mineral image. Experimental results on different types of microscopy images of minerals verified the effective performance of the proposed algorithm for microscopy mineral image enhancement. Copyright © 2012 Wiley Periodicals, Inc.

  1. 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.

  2. 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.

  3. 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.

  4. 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

  5. Evaluation of image features and classification methods for Barrett's cancer detection using VLE imaging

    NASA Astrophysics Data System (ADS)

    Klomp, Sander; van der Sommen, Fons; Swager, Anne-Fré; Zinger, Svitlana; Schoon, Erik J.; Curvers, Wouter L.; Bergman, Jacques J.; de With, Peter H. N.

    2017-03-01

    Volumetric Laser Endomicroscopy (VLE) is a promising technique for the detection of early neoplasia in Barrett's Esophagus (BE). VLE generates hundreds of high resolution, grayscale, cross-sectional images of the esophagus. However, at present, classifying these images is a time consuming and cumbersome effort performed by an expert using a clinical prediction model. This paper explores the feasibility of using computer vision techniques to accurately predict the presence of dysplastic tissue in VLE BE images. Our contribution is threefold. First, a benchmarking is performed for widely applied machine learning techniques and feature extraction methods. Second, three new features based on the clinical detection model are proposed, having superior classification accuracy and speed, compared to earlier work. Third, we evaluate automated parameter tuning by applying simple grid search and feature selection methods. The results are evaluated on a clinically validated dataset of 30 dysplastic and 30 non-dysplastic VLE images. Optimal classification accuracy is obtained by applying a support vector machine and using our modified Haralick features and optimal image cropping, obtaining an area under the receiver operating characteristic of 0.95 compared to the clinical prediction model at 0.81. Optimal execution time is achieved using a proposed mean and median feature, which is extracted at least factor 2.5 faster than alternative features with comparable performance.

  6. 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

  7. Electrophysiological features and multimodal imaging in ritonavir-related maculopathy.

    PubMed

    Faure, Céline; Paques, Michel; Audo, Isabelle

    2017-09-12

    The purpose of this study is to report a case of ritonavir-related retinal toxicity followed over a year. Electrophysiological features and multimodal imaging, including adaptive optics, are provided and discussed. Electrophysiological recordings and multimodal imaging were performed and repeated over 1 year. Fundus examination revealed crystalline maculopathy in conjunction with pigment disruption. Spectral domain optical coherence tomography displayed thinning of the macula without cysts. Autofluorescence imaging revealed a mixed pattern of complete loss of the autofluorescence in the area of retinal pigment deposit and an increased transmission of the autofluorescence in the area of retinal thinning. Fluorescein angiography ruled out parafoveal telangiectasia. Indocyanine green angiography was not contributive. Increased spacing of the macular cone mosaic, crystal deposits and pigment migrations were seen with adaptive optics. Full-field electroretinogram was slightly reduced for both eyes, especially in the light-adapted responses, and mfERG confirmed bilateral maculopathy. Functional and structural abnormalities did not change with follow-up besides constant pigmentary changes monitored with adaptive optics. Ritonavir-related retinal toxicity is a maculopathy with peculiar features including crystalline and pigment migration associated with central or temporofoveolar thinning and inconstant macular telangiectasia. Despite drug cessation, retinal remodelling continues to progress.

  8. 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

  9. 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.

  10. Nonparametric Joint Shape and Feature Priors for Image Segmentation.

    PubMed

    Erdil, Ertunc; Ghani, Muhammad Usman; Rada, Lavdie; Argunsah, Ali Ozgur; Unay, Devrim; Tasdizen, Tolga; Cetin, Mujdat

    2017-11-01

    In many image segmentation problems involving limited and low-quality data, employing statistical prior information about the shapes of the objects to be segmented can significantly improve the segmentation result. However, defining probability densities in the space of shapes is an open and challenging problem, especially if the object to be segmented comes from a shape density involving multiple modes (classes). Existing techniques in the literature estimate the underlying shape distribution by extending Parzen density estimator to the space of shapes. In these methods, the evolving curve may converge to a shape from a wrong mode of the posterior density when the observed intensities provide very little information about the object boundaries. In such scenarios, employing both shape- and class-dependent discriminative feature priors can aid the segmentation process. Such features may involve, e.g., intensity-based, textural, or geometric information about the objects to be segmented. In this paper, we propose a segmentation algorithm that uses nonparametric joint shape and feature priors constructed by Parzen density estimation. We incorporate the learned joint shape and feature prior distribution into a maximum a posteriori estimation framework for segmentation. The resulting optimization problem is solved using active contours. We present experimental results on a variety of synthetic and real data sets from several fields involving multimodal shape densities. Experimental results demonstrate the potential of the proposed method.

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

    PubMed

    Choudhry, Netan; Rao, Rajesh C

    2015-06-01

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

  12. 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

  13. Extraction of semantic features of histological images for content-based retrieval of images

    NASA Astrophysics Data System (ADS)

    Tang, Lilian H.; Hanka, Rudolf; Ip, Horace H. S.; Lam, Ringo

    1999-07-01

    This paper presents an approach for automatically assign histologically meaningful labels to tissue slide images. This approach is implemented as part of a larger system, I- Browse, which combines iconic and semantic content for intelligent image browsing. Our approach partitioned an input image into a number of subimages. A set of texture features based on Gabor filterings and color histogram which capture the visual characteristics of each of the subimages were computed. These image feature measurements then form the input to a pattern classifier which gives an initial coarse label assignment to subimages based on a hierarchical clustering of these image features. To facilitate supervised training of the classifier, a knowledge elicitation tool was developed which allows a histopathologist to assign histological terms to a sample of sub-images obtained from digitized tissue imags. The initial labels and their spatial distribution were then analyzed by a semantic analyzer with the help of a knowledge base which contains prior knowledge of the expected visual appearance of histological images of an organ. The label assigned to the subimages were successive refined through a process of relevant feedback.

  14. 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

  15. Osteosarcoma of the jaws: demographic and CT imaging features

    PubMed Central

    Wang, S; Shi, H; Yu, Q

    2012-01-01

    Objective The aim of this study was to evaluate the patient demographic and CT imaging findings of primary osteosarcoma of the jaws. Methods 88 primary osteosarcomas of the jaws histopathologically diagnosed during 1997–2007 were reviewed. 21 cases of CT images were reviewed. Results Of 88 patients, 51 (58%) had tumours in the mandible and 37 (42%) in the maxilla. The mean age was 37.8 years (range 9–80 years). The male-to-female ratio was 1.32:1. The mean age of patients with mandibular lesions was 41.04 years and in those with maxillary lesions it was 33.3 years. CT imaging findings were available in 21 patients. In the maxilla (n = 9), all tumours (100%) arose from the alveolar ridge. In the mandible (n = 12), most tumours (9 cases, 75%), arose from the ramus and/or condyle. All except two lesions had the epicentrum within the medullary cavity of the involved bone. The presence of periosteal reaction was demonstrated in 13 cases (62%). Soft-tissue extension was present in 18 lesions (86%), with calcification identified in 13 (72%). Conclusions This study provides age, sex distribution, location and CT imaging features of primary osteosarcoma of the jaws. PMID:22074870

  16. 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.

  17. Automatic evaluation of skin histopathological images for melanocytic features

    NASA Astrophysics Data System (ADS)

    Koosha, Mohaddeseh; Hoseini Alinodehi, S. Pourya; Nicolescu, Mircea; Safaei Naraghi, Zahra

    2017-03-01

    Successfully detecting melanocyte cells in the skin epidermis has great significance in skin histopathology. Because of the existence of cells with similar appearance to melanocytes in hematoxylin and eosin (HE) images of the epidermis, detecting melanocytes becomes a challenging task. This paper proposes a novel technique for the detection of melanocytes in HE images of the epidermis, based on the melanocyte color features, in the HSI color domain. Initially, an effective soft morphological filter is applied to the HE images in the HSI color domain to remove noise. Then a novel threshold-based technique is applied to distinguish the candidate melanocytes' nuclei. Similarly, the method is applied to find the candidate surrounding halos of the melanocytes. The candidate nuclei are associated with their surrounding halos using the suggested logical and statistical inferences. Finally, a fuzzy inference system is proposed, based on the HSI color information of a typical melanocyte in the epidermis, to calculate the similarity ratio of each candidate cell to a melanocyte. As our review on the literature shows, this is the first method evaluating epidermis cells for melanocyte similarity ratio. Experimental results on various images with different zooming factors show that the proposed method improves the results of previous works.

  18. Wavelength calibration of imaging spectrometer using atmospheric absorption features

    NASA Astrophysics Data System (ADS)

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

    2012-11-01

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

  19. 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.

  20. 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).

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

    PubMed

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

    2016-05-07

    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.

  2. 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.

  3. 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.

  4. Feature coding in image classification: a comprehensive study.

    PubMed

    Huang, Yongzhen; Wu, Zifeng; Wang, Liang; Tan, Tieniu

    2014-03-01

    Image classification is a hot topic in computer vision and pattern recognition. Feature coding, as a key component of image classification, has been widely studied over the past several years, and a number of coding algorithms have been proposed. However, there is no comprehensive study concerning the connections between different coding methods, especially how they have evolved. In this paper, we first make a survey on various feature coding methods, including their motivations and mathematical representations, and then exploit their relations, based on which a taxonomy is proposed to reveal their evolution. Further, we summarize the main characteristics of current algorithms, each of which is shared by several coding strategies. Finally, we choose several representatives from different kinds of coding approaches and empirically evaluate them with respect to the size of the codebook and the number of training samples on several widely used databases (15-Scenes, Caltech-256, PASCAL VOC07, and SUN397). Experimental findings firmly justify our theoretical analysis, which is expected to benefit both practical applications and future research.

  5. 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.

  6. 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

  7. Featured Image: A Detailed Look at the Crab Nebula

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2017-07-01

    Planning on watching fireworks tomorrow? Heres an astronomical firework to help you start the celebrations! A new study has stunningly detailed the Crab Nebula (click for a closer look), a nebula 6,500 light-years away thought to have been formedby a supernova explosion and the subsequent ultrarelativistic wind emitted by the pulsar at its heart. Led by Gloria Dubner (University of Buenos Aires), the authors of this study obtained new observations of the Crab Nebula from five different telescopes. They compiled these observations to compare the details of the nebulas structure across different wavelengths, which allowedthem to learnabout the sources of various features within the nebula. In the images above, thetop left shows the 3 GHz data from the Very Large Array (radio). Moving clockise, the radio data (shown in red) is composited with: infrared data from Spitzer Space Telescope, optical continuum from Hubble Space Telescope, 500-nm optical datafrom Hubble, and ultraviolet data from XMM-Newton. The final two images are of the nebula center, and they are composites of the radio imagewith X-ray data from Chandra and near-infrared data from Hubble. To read more about what Dubner and collaborators learned (and to see more spectacular images!), check out the paper below.CitationG. Dubner et al 2017 ApJ 840 82. doi:10.3847/1538-4357/aa6983

  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. 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

  10. 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.

  11. 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.

  12. Digital image analysis supports a nuclear-to-cytoplasmic ratio cutoff value of 0.5 for atypical urothelial cells.

    PubMed

    Hang, Jen-Fan; Charu, Vivek; Zhang, M Lisa; VandenBussche, Christopher J

    2017-09-01

    An elevated nuclear-to-cytoplasmic (N:C) ratio of ≥0.5 is a required criterion for the diagnosis of atypical urothelial cells (AUC) in The Paris System for Reporting Urinary Cytology. To validate the N:C ratio cutoff value and its predictive power for high-grade urothelial carcinoma (HGUC), the authors retrospectively reviewed the urinary tract cytology specimens of 15 cases of AUC with HGUC on follow-up (AUC-HGUC) and 33 cases of AUC without HGUC on follow-up (AUC-N-HGUC). The number of atypical cells in each case was recorded, and each atypical cell was photographed and digitally examined to calculate the nuclear size and N:C ratio. On average, the maximum N:C ratios of atypical cells were significantly different between the AUC-HGUC and AUC-N-HGUC cohorts (0.53 vs 0.43; P =.00009), whereas the maximum nuclear sizes of atypical cells (153.43 μM(2) vs 201.47 μM(2) ; P = .69) and the number of atypical cells per case (10.13 vs 7.88; P = .12) were not found to be significantly different. Receiver operating characteristic analysis demonstrated that the maximum N:C ratio alone had high discriminatory capacity (area under the curve, 79.19%; 95% confidence interval, 64.19%-94.19%). The optimal maximum N:C ratio threshold was 0.486, giving a sensitivity of 73.3% and a specificity of 84.8% for predicting HGUC on follow-up. The identification of AUC with an N:C ratio >0.486 has a high predictive power for HGUC on follow-up in AUC specimens. This justifies using the N:C ratio as a required criterion for the AUC category. Individual laboratories using different cytopreparation methods may require independent validation of the N:C ratio cutoff value. Cancer Cytopathol 2017;125:710-6. © 2017 American Cancer Society. © 2017 American Cancer Society.

  13. 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. In conclusion, 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. 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.

  15. 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

  16. 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.

  17. 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.

  18. Multi-parametric MR imaging of transition zone prostate cancer: Imaging features, detection and staging

    PubMed Central

    Kayhan, Arda; Fan, Xiaobing; Oommen, Jacob; Oto, Aytekin

    2010-01-01

    Magnetic resonance (MR) imaging has been increasingly used in the evaluation of prostate cancer. As studies have suggested that the majority of cancers arise from the peripheral zone (PZ), MR imaging has focused on the PZ of the prostate gland thus far. However, a considerable number of cancers (up to 30%) originate in the transition zone (TZ), substantially contributing to morbidity and mortality. Therefore, research is needed on the TZ of the prostate gland. Recently, MR imaging and advanced MR techniques have been gaining acceptance in evaluation of the TZ. In this article, the MR imaging features of TZ prostate cancers, the role of MR imaging in TZ cancer detection and staging, and recent advanced MR techniques will be discussed in light of the literature. PMID:21161033

  19. 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.

  20. Comparison of outcomes after typical and atypical eclampsia: a retrospective study.

    PubMed

    Shin, Jae Eun; Nam, Sun Young; Lee, Young; Lee, Guisera; Shin, Jong Chul; Kim, Yeon Hee; Kil, Ki Cheol

    2012-11-01

    To evaluate the characteristics, clinical features and maternal-perinatal outcomes after atypical eclampsia. In a retrospective study, we compared demographics, clinical characteristics and outcomes between typical and atypical eclampsia. Of 90 eclamptic patients, 56 had typical eclamptic features and 34 had atypical features. Compared to typical eclampsia, atypical eclampsia had higher gestational age (37.6 ± 3.3 vs. 34.6 ± 4.2 weeks, p = 0.001), a higher incidence of no antenatal risk factors [25 (73.5%) vs. 12 (21.4%), p < 0.001], less antepartum seizures [11 (32.4%) vs. 45 (80.4%), p < 0.001], a lower incidence of prodromal symptoms [20 (58.5%) vs. 49 (87.5%), p = 0.002], and a higher incidence of no lesion in brain imaging [16 (47.1%) vs. 12 (21.4%), p = 0.010). Although atypical eclampsia was associated with a lower odd ratio (OR) in composite perinatal complications (OR = 0.22, 95% CI = 0.08-0.60, p = 0.003), composite maternal complications did not differ between the two groups (OR = 0.52, 95% CI = 0.08-0.60, p =0.191). Maternal outcomes did not differ between the two groups. Therefore, more attention should be focused on atypical eclampsia.

  1. Spine MR imaging features of subacute combined degeneration patients.

    PubMed

    Sun, Hye Young; Lee, Joon Woo; Park, Kyung Seok; Wi, Jae Yeon; Kang, Heung Sik

    2014-05-01

    Subacute combined degeneration (SCD) is a potentially reversible neurological complication of a vitamin B12 deficiency; therefore, timely diagnosis and appropriate treatment are of great importance. The study was to evaluate the spine MR imaging features of SCD in a series of patients. Eight patients diagnosed with SCD from 2008 to 2010 comprised the study population. Spine MRIs were available for all eight patients, and three of them had follow-up MRIs after vitamin B12 treatment. Two radiologists evaluated the prevalence of signal intensity abnormality of spinal cord and analyzed the distribution and pattern of the signal change in consensus. And they also evaluated post-treatment MRI to find interval change. Seven of eight patients showed abnormal hyperintensity within posterior aspect of spinal cord on T2-weighted images. The spinal cord abnormalities were seen at cervical spine in five patients (62.5 %) and at thoracic spine in the other two patients (25 %). For patients with cervical spinal cord abnormalities, axial T2-weighted images showed symmetric linear T2-hyperintensity as an "inverted V" at cervical spinal cord. For patients with thoracic spinal cord abnormalities, the abnormal signal intensity looked bilateral paired nodular T2-hyperintensity as "dumbbell" or "binoculars" at thoracic spinal cord. Follow-up MRIs after vitamin B12 treatment showed interval resolution of the areas of abnormal T2-hyperintensity in all. Symmetric T2-hyperintensity within dorsal column of spinal cord is commonly seen in SCD patients with a linear pattern in the cervical spine and a nodular pattern in the thoracic spine.

  2. 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.

  3. 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

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

    NASA Astrophysics Data System (ADS)

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

    2006-01-01

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

  5. 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.

  6. Multi-scale contrast enhancement of oriented features in 2D images using directional morphology

    NASA Astrophysics Data System (ADS)

    Das, Debashis; Mukhopadhyay, Susanta; Praveen, S. R. Sai

    2017-01-01

    This paper presents a multi-scale contrast enhancement scheme for improving the visual quality of directional features present in 2D gray scale images. Directional morphological filters are employed to locate and extract the scale-specific image features with different orientations which are subsequently stored in a set of feature images. The final enhanced image is constructed by weighted combination of these feature images with the original image. While construction, the feature images corresponding to progressively smaller scales are made to have higher proportion of contribution through the use of progressively larger weights. The proposed method has been formulated, implemented and executed on a set of real 2D gray scale images with oriented features. The experimental results visually establish the efficacy of the method. The proposed method has been compared with other similar methods both on subjective and objective basis and the overall performance is found to be satisfactory.

  7. 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.

  8. Two-level evaluation on sensor interoperability of features in fingerprint image segmentation.

    PubMed

    Yang, Gongping; Li, Ying; Yin, Yilong; Li, Ya-Shuo

    2012-01-01

    Features used in fingerprint segmentation significantly affect the segmentation performance. Various features exhibit different discriminating abilities on fingerprint images derived from different sensors. One feature which has better discriminating ability on images derived from a certain sensor may not adapt to segment images derived from other sensors. This degrades the segmentation performance. This paper empirically analyzes the sensor interoperability problem of segmentation feature, which refers to the feature's ability to adapt to the raw fingerprints captured by different sensors. To address this issue, this paper presents a two-level feature evaluation method, including the first level feature evaluation based on segmentation error rate and the second level feature evaluation based on decision tree. The proposed method is performed on a number of fingerprint databases which are obtained from various sensors. Experimental results show that the proposed method can effectively evaluate the sensor interoperability of features, and the features with good evaluation results acquire better segmentation accuracies of images originating from different sensors.

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

    SciTech Connect

    Kwong, M.K.; Lin, B.

    1995-07-01

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

  10. Hemorrhage in posterior reversible encephalopathy syndrome: imaging and clinical features.

    PubMed

    Hefzy, H M; Bartynski, W S; Boardman, J F; Lacomis, D

    2009-08-01

    Hemorrhage is known to occur in posterior reversible encephalopathy syndrome (PRES), but the characteristics have not been analyzed in detail. The purpose of this study was to evaluate the imaging and clinical features of hemorrhage in PRES. Retrospective assessment of 151 patients with PRES was performed, and 23 patients were identified who had intracranial hemorrhage at toxicity. Hemorrhage types were identified and tabulated, including minute focal hemorrhages (<5 mm), sulcal subarachnoid hemorrhage, and focal hematoma. Clinical features of hemorrhage and nonhemorrhage PRES groups were evaluated, including toxicity blood pressure, coagulation profile/platelet counts, coagulation-altering medication, and clinical conditions associated with PRES. Toxicity mean arterial pressure (MAP) groups were defined as normal (<106 mm Hg), mildly hypertensive (106-116 mm Hg), or severely hypertensive (>116 mm Hg). The overall incidence of hemorrhage was 15.2%, with borderline statistical significance noted between the observed clinical associations (P = .07). Hemorrhage was significantly more common (P = .02) after allogeneic bone marrow transplantation (allo-BMT) than after solid-organ transplantation. The 3 hemorrhage types were noted with equal frequency. A single hemorrhage type was found in 16 patients, with multiple types noted in 7. Patients undergoing therapeutic anticoagulation were statistically more likely to develop hemorrhage (P = .04). No difference in hemorrhage incidence was found among the 3 blood pressure subgroups (range, 14.9%-15.9%). Three distinct types of hemorrhage (minute hemorrhage, sulcal subarachnoid hemorrhage, hematoma) were identified in PRES with equal frequency. The greatest hemorrhage frequency was seen after allo-BMT and in patients undergoing therapeutic anticoagulation. Hemorrhage rate was independent of the toxicity blood pressure.

  11. Fractal Analysis May Improve the Preoperative Identification of Atypical Meningiomas.

    PubMed

    Czyz, Marcin; Radwan, Hesham; Li, Jian Y; Filippi, Christopher G; Tykocki, Tomasz; Schulder, Michael

    2017-02-01

    There is no objective and readily accessible method for the preoperative determination of atypical characteristics of a meningioma grade. To evaluate the feasibility of using fractal analysis as an adjunctive tool to conventional radiological techniques in visualizing histopathological features of meningiomas. A group of 27 patients diagnosed with atypical (WHO grade II) meningioma and a second group of 27 patients with benign (WHO grade I) meningioma were enrolled in the study. Preoperative brain magnetic resonance (MR) studies (T1-wieghted, post-gadolinium) were processed and analyzed to determine the average fractal dimension (FDa) and maximum fractal dimension (FDm) of the contrast-enhancing region of the tumor using box-count method. FDa and FDm as well as particular radiological features were included in the logistic regression model as possible predictors of malignancy. The cohort consisted of 34 women and 20 men, mean age of 62 ± 15 yr. Fractal analysis showed good interobserver reproducibility (Kappa >0.70). Both FDa and FDm were significantly higher in the atypical compared to the benign meningioma group (P < .0001). Multivariate logistic regression model reached statistical significance with P = .0001 and AUC = 0.87. The FDm, which was greater than 1.31 (odds ratio [OR], 12.30; P = .039), and nonskull base localization (OR, .052; P = .015) were confirmed to be statistically significant predictors of the atypical phenotype. Fractal analysis of preoperative MR images appears to be a feasible adjunctive diagnostic tool in identifying meningiomas with potentially aggressive clinical behavior.

  12. A feature-preserving hair removal algorithm for dermoscopy images.

    PubMed

    Abbas, Qaisar; Garcia, Irene Fondón; Emre Celebi, M; Ahmad, Waqar

    2013-02-01

    Accurate segmentation and repair of hair-occluded information from dermoscopy images are challenging tasks for computer-aided detection (CAD) of melanoma. Currently, many hair-restoration algorithms have been developed, but most of these fail to identify hairs accurately and their removal technique is slow and disturbs the lesion's pattern. In this article, a novel hair-restoration algorithm is presented, which has a capability to preserve the skin lesion features such as color and texture and able to segment both dark and light hairs. Our algorithm is based on three major steps: the rough hairs are segmented using a matched filtering with first derivative of gaussian (MF-FDOG) with thresholding that generate strong responses for both dark and light hairs, refinement of hairs by morphological edge-based techniques, which are repaired through a fast marching inpainting method. Diagnostic accuracy (DA) and texture-quality measure (TQM) metrics are utilized based on dermatologist-drawn manual hair masks that were used as a ground truth to evaluate the performance of the system. The hair-restoration algorithm is tested on 100 dermoscopy images. The comparisons have been done among (i) linear interpolation, inpainting by (ii) non-linear partial differential equation (PDE), and (iii) exemplar-based repairing techniques. Among different hair detection and removal techniques, our proposed algorithm obtained the highest value of DA: 93.3% and TQM: 90%. The experimental results indicate that the proposed algorithm is highly accurate, robust and able to restore hair pixels without damaging the lesion texture. This method is fully automatic and can be easily integrated into a CAD system. © 2011 John Wiley & Sons A/S.

  13. Atypical lobular hyperplasia and lobular carcinoma in situ at core needle biopsy of the breast: An incidental finding or are there characteristic imaging findings?

    PubMed

    Amos, Barry; Chetlen, Alison; Williams, Nicole

    2016-01-25

    Atypical lobular hyperplasia and classic-type lobular carcinoma in situ, collectively known as lobular neoplasia, are classically described as incidental findings found on breast core-needle biopsy without distinguishing imaging characteristics. The purpose of this study was to investigate concordant imaging findings of lobular neoplasia identified at core-needle biopsy after careful radiologic-pathologic correlation. The pathology database was searched from October 1, 2006 to October 1, 2013 for breast biopsies yielding lobular neoplasia not associated with a coexistent malignancy or other high risk lesion in the biopsy specimen. Of the 482 biopsies performed containing lobular neoplasia, 65 cases had lobular neoplasia as the highest risk lesion at core-needle biopsy. Of the 65 total cases in which lobular neoplasia was the highest risk lesion, 18 (28%) cases had concordant imaging correlates. 13 of 18 (72%) cases presented as calcifications on mammography and 5 of 18 (28%) presented on magnetic resonance imaging as a focus (n = 2) or non-mass enhancement (n = 3). With careful radiologic-pathologic correlation, mammographically detected calcifications and foci or non-mass enhancement on magnetic resonance imaging can be considered concordant imaging findings of lobular neoplasia after breast core-needle biopsy.

  14. 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.

  15. 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

  16. 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.

  17. 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.

  18. 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.

  19. 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.…

  20. 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.…

  1. 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

  2. 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 Cd (whichmore » 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. In conclusion, 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

  3. 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.

  4. 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.

  5. Feature analysis for classification of trace fluorescent labeled protein crystallization images.

    PubMed

    Sigdel, Madhav; Dinc, Imren; Sigdel, Madhu S; Dinc, Semih; Pusey, Marc L; Aygun, Ramazan S

    2017-01-01

    Large number of features are extracted from protein crystallization trial images to improve the accuracy of classifiers for predicting the presence of crystals or phases of the crystallization process. The excessive number of features and computationally intensive image processing methods to extract these features make utilization of automated classification tools on stand-alone computing systems inconvenient due to the required time to complete the classification tasks. Combinations of image feature sets, feature reduction and classification techniques for crystallization images benefiting from trace fluorescence labeling are investigated. Features are categorized into intensity, graph, histogram, texture, shape adaptive, and region features (using binarized images generated by Otsu's, green percentile, and morphological thresholding). The effects of normalization, feature reduction with principle components analysis (PCA), and feature selection using random forest classifier are also analyzed. The time required to extract feature categories is computed and an estimated time of extraction is provided for feature category combinations. We have conducted around 8624 experiments (different combinations of feature categories, binarization methods, feature reduction/selection, normalization, and crystal categories). The best experimental results are obtained using combinations of intensity features, region features using Otsu's thresholding, region features using green percentile G90 thresholding, region features using green percentile G99 thresholding, graph features, and histogram features. Using this feature set combination, 96% accuracy (without misclassifying crystals as non-crystals) was achieved for the first level of classification to determine presence of crystals. Since missing a crystal is not desired, our algorithm is adjusted to achieve a high sensitivity rate. In the second level classification, 74.2% accuracy for (5-class) crystal sub

  6. 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.

  7. A study of T2-weighted MR image texture features and diffusion-weighted MR image features for computer-aided diagnosis of prostate cancer

    NASA Astrophysics Data System (ADS)

    Peng, Yahui; Jiang, Yulei; Antic, Tatjana; Giger, Maryellen L.; Eggener, Scott; Oto, Aytekin

    2013-02-01

    The purpose of this study was to study T2-weighted magnetic resonance (MR) image texture features and diffusionweighted (DW) MR image features in distinguishing prostate cancer (PCa) from normal tissue. We collected two image datasets: 23 PCa patients (25 PCa and 23 normal tissue regions of interest [ROIs]) imaged with Philips MR scanners, and 30 PCa patients (41 PCa and 26 normal tissue ROIs) imaged with GE MR scanners. A radiologist drew ROIs manually via consensus histology-MR correlation conference with a pathologist. A number of T2-weighted texture features and apparent diffusion coefficient (ADC) features were investigated, and linear discriminant analysis (LDA) was used to combine select strong image features. Area under the receiver operating characteristic (ROC) curve (AUC) was used to characterize feature effectiveness in distinguishing PCa from normal tissue ROIs. Of the features studied, ADC 10th percentile, ADC average, and T2-weighted sum average yielded AUC values (+/-standard error) of 0.95+/-0.03, 0.94+/-0.03, and 0.85+/-0.05 on the Phillips images, and 0.91+/-0.04, 0.89+/-0.04, and 0.70+/-0.06 on the GE images, respectively. The three-feature combination yielded AUC values of 0.94+/-0.03 and 0.89+/-0.04 on the Phillips and GE images, respectively. ADC 10th percentile, ADC average, and T2-weighted sum average, are effective in distinguishing PCa from normal tissue, and appear robust in images acquired from Phillips and GE MR scanners.

  8. 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.

  9. Different and identical features of chondroblastic osteosarcoma and chondrosarcoma: highlights on radiography and magnetic resonance imaging.

    PubMed

    Yen, Chao-Hsuan; Chang, Cheng-Yen; Teng, Michael Mu-Huo; Wu, Hung-Ta H; Chen, Paul Chih-Hsueh; Chiou, Hong-Jen; Chiu, Nai-Chi

    2009-02-01

    To identify the different and identical features of 2 tumors with similar pathologic findings, chondroblastic osteosarcoma (OGS) and chondrosarcoma (CSA), with highlights on radiography and magnetic resonance imaging (MRI). Ten patients with chondroblastic OGS and 10 patients with CSA were enrolled. After recording the tumor location, tumor morphology was evaluated for patterns of bony destruction, visible tumor matrix, and aggressive periosteal reactions, endosteal scalloping, cortical expansion, cortical breakthrough and pathologic fracture by radiographic analysis. Signal intensity changes, enhancement pattern, and tumor extensions were evaluated by MRI. The mean patient ages were 24.7 and 56.7 years in patients with chondroblastic OGS and CSA, respectively (p = 0.001). Tumor occurrence was detected in the appendicular bones in 8 chondroblastic OGS and 3 CSA. Three chondroblastic OGS occurred around the knee (p = 0.003). In addition, there were 6 tumors arising from the metaphysis and 2 arising from the diaphysis in chondroblastic OGS patients. In CSA patients, 1 tumor arose in the metaphysis, 1 in the diaphysis, and 1 in the epiphysis (p = 0.039). On radiographs, visible bone-forming tumor matrix was present in 8 chondroblastic OGS, and coexistence of bone- and cartilage-forming patterns were detected in 2. Visible cartilage-forming tumor matrix was present in 7 CSA, and atypical radiodensity patterns were detected in 2 (p < 0.001). Aggressive periosteal reaction was present in 7 chondroblastic OGS, and non-aggressive periosteal reaction was found in 1 CSA (p = 0.008). MRI revealed the presence of a lobular structure of high signal intensity on T2-weighted images, and peripheral rim and septal enhancement pattern was noted in 2 chondroblastic OGS and 10 CSA patients. Inhomogeneous and marginal enhancement patterns were noted in 6 and 2 chondroblastic OGS, respectively (p = 0.001). Metaphysis origin, bone-forming tumor matrix, aggressive periosteal reaction, and

  10. 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.

  11. 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.

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

    PubMed

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

    2015-10-05

    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

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

    PubMed Central

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

    2016-01-01

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

  14. 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.

  15. 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.

  16. 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

  17. 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.

  18. Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features.

    PubMed

    Xu, Yan; Jia, Zhipeng; Wang, Liang-Bo; Ai, Yuqing; Zhang, Fang; Lai, Maode; Chang, Eric I-Chao

    2017-05-26

    Histopathology image analysis is a gold standard for cancer recognition and diagnosis. Automatic analysis of histopathology images can help pathologists diagnose tumor and cancer subtypes, alleviating the workload of pathologists. There are two basic types of tasks in digital histopathology image analysis: image classification and image segmentation. Typical problems with histopathology images that hamper automatic analysis include complex clinical representations, limited quantities of training images in a dataset, and the extremely large size of singular images (usually up to gigapixels). The property of extremely large size for a single image also makes a histopathology image dataset be considered large-scale, even if the number of images in the dataset is limited. In this paper, we propose leveraging deep convolutional neural network (CNN) activation features to perform classification, segmentation and visualization in large-scale tissue histopathology images. Our framework transfers features extracted from CNNs trained by a large natural image database, ImageNet, to histopathology images. We also explore the characteristics of CNN features by visualizing the response of individual neuron components in the last hidden layer. Some of these characteristics reveal biological insights that have been verified by pathologists. According to our experiments, the framework proposed has shown state-of-the-art performance on a brain tumor dataset from the MICCAI 2014 Brain Tumor Digital Pathology Challenge and a colon cancer histopathology image dataset. The framework proposed is a simple, efficient and effective system for histopathology image automatic analysis. We successfully transfer ImageNet knowledge as deep convolutional activation features to the classification and segmentation of histopathology images with little training data. CNN features are significantly more powerful than expert-designed features.

  19. 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

  20. Relationship between atypical depression and social anxiety disorder.

    PubMed

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

    2015-01-30

    In this study, we aimed to investigate the effects of atypical and non-atypical depression comorbidity on the clinical characteristics and course of social anxiety disorder (SAD). A total of 247 patients with SAD were enrolled: 145 patients with a current depressive episode (unipolar or bipolar) with atypical features, 43 patients with a current depressive episode with non-atypical features and 25 patients without a lifetime history of depressive episodes were compared regarding sociodemographic and clinical features, comorbidity rates, and severity of SAD, depression and functional impairment. Thirty four patients with a past but not current history of major depressive episodes were excluded from the comparisons. 77.1% of current depressive episodes were associated with atypical features. Age at onset of SAD and age at initial major depressive episode were lower in the group with atypical depression than in the group with non-atypical depression. History of suicide attempts and bipolar disorder comorbidity was more common in the atypical depression group as well. Atypical depression group has higher SAD and depression severity and lower functionality than group with non-atypical depression. Our results indicate that the presence of atypical depression is associated with more severe symptoms and more impairment in functioning in patients with SAD. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  1. 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.

  2. 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.

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

    SciTech Connect

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

    1996-06-01

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

  4. 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

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

    SciTech Connect

    Kitsak, M A; Kitsak, A I

    2010-12-09

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

  6. 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.

  7. 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.

  8. Applying Quantitative CT Image Feature Analysis to Predict Response of Ovarian Cancer Patients to Chemotherapy.

    PubMed

    Danala, Gopichandh; Thai, Theresa; Gunderson, Camille C; Moxley, Katherine M; Moore, Kathleen; Mannel, Robert S; Liu, Hong; Zheng, Bin; Qiu, Yuchen

    2017-10-01

    The study aimed to investigate the role of applying quantitative image features computed from computed tomography (CT) images for early prediction of tumor response to chemotherapy in the clinical trials for treating ovarian cancer patients. A dataset involving 91 patients was retrospectively assembled. Each patient had two sets of pre- and post-therapy CT images. A computer-aided detection scheme was applied to segment metastatic tumors previously tracked by radiologists on CT images and computed image features. Two initial feature pools were built using image features computed from pre-therapy CT images only and image feature difference computed from both pre- and post-therapy images. A feature selection method was applied to select optimal features, and an equal-weighted fusion method was used to generate a new quantitative imaging marker from each pool to predict 6-month progression-free survival. The prediction accuracy between quantitative imaging markers and the Response Evaluation Criteria in Solid Tumors (RECIST) criteria was also compared. The highest areas under the receiver operating characteristic curve are 0.684 ± 0.056 and 0.771 ± 0.050 when using a single image feature computed from pre-therapy CT images and feature difference computed from pre- and post-therapy CT images, respectively. Using two corresponding fusion-based image markers, the areas under the receiver operating characteristic curve significantly increased to 0.810 ± 0.045 and 0.829 ± 0.043 (P < 0.05), respectively. Overall prediction accuracy levels are 71.4%, 80.2%, and 74.7% when using two imaging markers and RECIST, respectively. This study demonstrated the feasibility of predicting patients' response to chemotherapy using quantitative imaging markers computed from pre-therapy CT images. However, using image feature difference computed between pre- and post-therapy CT images yielded higher prediction accuracy. Copyright © 2017 The Association of University

  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. 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.

  11. 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.

  12. 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.

  13. 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.

  14. Target feature-enhanced of SAR image based on regularization of lk norm

    NASA Astrophysics Data System (ADS)

    Wang, Xiong-liang; Wang, Zheng-ming; Wang, Chun-ling

    2005-11-01

    Target feature-enhanced processing of SAR image is meaningful to SAR ATR. One regularization method based on lk norm used for target feature-enhanced of SAR image is discussed in this paper. This method exploits the useful sparse prior information which is well consistent to the statistically property of SAR image, makes up the additional constraint condition, turns the problem of target feature-enhanced processing of SAR image into the simple-formed optimization problem. A fast iterative algorithm is proposed to solve the optimization problem. The Simulation results and computational results of measured data prove its validity.

  15. 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.

  16. 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

  17. Joint analysis of histopathology image features and gene expression in breast cancer.

    PubMed

    Popovici, Vlad; Budinská, Eva; Čápková, Lenka; Schwarz, Daniel; Dušek, Ladislav; Feit, Josef; Jaggi, Rolf

    2016-05-11

    Genomics and proteomics are nowadays the dominant techniques for novel biomarker discovery. However, histopathology images contain a wealth of information related to the tumor histology, morphology and tumor-host interactions that is not accessible through these techniques. Thus, integrating the histopathology images in the biomarker discovery workflow could potentially lead to the identification of new image-based biomarkers and the refinement or even replacement of the existing genomic and proteomic signatures. However, extracting meaningful and robust image features to be mined jointly with genomic (and clinical, etc.) data represents a real challenge due to the complexity of the images. We developed a framework for integrating the histopathology images in the biomarker discovery workflow based on the bag-of-features approach - a method that has the advantage of being assumption-free and data-driven. The images were reduced to a set of salient patterns and additional measurements of their spatial distribution, with the resulting features being directly used in a standard biomarker discovery application. We demonstrated this framework in a search for prognostic biomarkers in breast cancer which resulted in the identification of several prognostic image features and a promising multimodal (imaging and genomic) prognostic signature. The source code for the image analysis procedures is freely available. The framework proposed allows for a joint analysis of images and gene expression data. Its application to a set of breast cancer cases resulted in image-based and combined (image and genomic) prognostic scores for relapse-free survival.

  18. 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.

  19. 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

  20. 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…

  1. 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…

  2. Large Margin Multi-Modal Multi-Task Feature Extraction for Image Classification.

    PubMed

    Yong Luo; Yonggang Wen; Dacheng Tao; Jie Gui; Chao Xu

    2016-01-01

    The features used in many image analysis-based applications are frequently of very high dimension. Feature extraction offers several advantages in high-dimensional cases, and many recent studies have used multi-task feature extraction approaches, which often outperform single-task feature extraction approaches. However, most of these methods are limited in that they only consider data represented by a single type of feature, even though features usually represent images from multiple modalities. We, therefore, propose a novel large margin multi-modal multi-task feature extraction (LM3FE) framework for handling multi-modal features for image classification. In particular, LM3FE simultaneously learns the feature extraction matrix for each modality and the modality combination coefficients. In this way, LM3FE not only handles correlated and noisy features, but also utilizes the complementarity of different modalities to further help reduce feature redundancy in each modality. The large margin principle employed also helps to extract strongly predictive features, so that they are more suitable for prediction (e.g., classification). An alternating algorithm is developed for problem optimization, and each subproblem can be efficiently solved. Experiments on two challenging real-world image data sets demonstrate the effectiveness and superiority of the proposed method.

  3. 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

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

    PubMed

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

    2015-01-01

    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. 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). 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.

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

    PubMed Central

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

    2014-01-01

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

  6. Deep pelvic endometriosis: a radiologist's guide to key imaging features with clinical and histopathologic review.

    PubMed

    Darvishzadeh, Ayeh; McEachern, Wendaline; Lee, Thomas K; Bhosale, Priya; Shirkhoda, Ali; Menias, Christine; Lall, Chandana

    2016-12-01

    While endometriosis typically affects the ovaries, deep infiltrating endometriosis can affect the gastrointestinal tract, urinary tract, and deep pelvis, awareness of which is important for radiologists. Symptoms are nonspecific and can range from chronic abdominal and deep pelvic pain to nausea, vomiting, diarrhea, constipation, hematuria, and rectal bleeding. Ultrasound and computed tomography may show nonspecific soft-tissue density masses causing bowel obstruction and hydronephrosis. This constellation of presenting symptoms and imaging evidence is easily mistaken for other pathologies including infectious gastroenteritis, diverticulitis, appendicitis, and malignancy, which may lead to unnecessary surgery or mismanagement. With this, deep pelvic endometriosis should be considered in the differential diagnosis in a female patient of reproductive age who presents with such atypical symptoms, and further work up with magnetic resonance imaging is imperative for accurate diagnosis, treatment selection, and preoperative planning.

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

    PubMed Central

    Choi, Jin-Young; Lee, Jeong-Min

    2014-01-01

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

  8. 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.

  9. 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.

  10. 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

  11. Blind image quality assessment based on aesthetic and statistical quality-aware features

    NASA Astrophysics Data System (ADS)

    Jenadeleh, Mohsen; Masaeli, Mohammad Masood; Moghaddam, Mohsen Ebrahimi

    2017-07-01

    The main goal of image quality assessment (IQA) methods is the emulation of human perceptual image quality judgments. Therefore, the correlation between objective scores of these methods with human perceptual scores is considered as their performance metric. Human judgment of the image quality implicitly includes many factors when assessing perceptual image qualities such as aesthetics, semantics, context, and various types of visual distortions. The main idea of this paper is to use a host of features that are commonly employed in image aesthetics assessment in order to improve blind image quality assessment (BIQA) methods accuracy. We propose an approach that enriches the features of BIQA methods by integrating a host of aesthetics image features with the features of natural image statistics derived from multiple domains. The proposed features have been used for augmenting five different state-of-the-art BIQA methods, which use statistical natural scene statistics features. Experiments were performed on seven benchmark image quality databases. The experimental results showed significant improvement of the accuracy of the methods.

  12. 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.

  13. 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

  14. 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

  15. 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.

  16. 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.

  17. 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

  18. 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.

  19. Ensemble classification of colon biopsy images based on information rich hybrid features.

    PubMed

    Rathore, Saima; Hussain, Mutawarra; Aksam Iftikhar, Muhammad; Jalil, Abdul

    2014-04-01

    In recent years, classification of colon biopsy images has become an active research area. Traditionally, colon cancer is diagnosed using microscopic analysis. However, the process is subjective and leads to considerable inter/intra observer variation. Therefore, reliable computer-aided colon cancer detection techniques are in high demand. In this paper, we propose a colon biopsy image classification system, called CBIC, which benefits from discriminatory capabilities of information rich hybrid feature spaces, and performance enhancement based on ensemble classification methodology. Normal and malignant colon biopsy images differ with each other in terms of the color distribution of different biological constituents. The colors of different constituents are sharp in normal images, whereas the colors diffuse with each other in malignant images. In order to exploit this variation, two feature types, namely color components based statistical moments (CCSM) and Haralick features have been proposed, which are color components based variants of their traditional counterparts. Moreover, in normal colon biopsy images, epithelial cells possess sharp and well-defined edges. Histogram of oriented gradients (HOG) based features have been employed to exploit this information. Different combinations of hybrid features have been constructed from HOG, CCSM, and Haralick features. The minimum Redundancy Maximum Relevance (mRMR) feature selection method has been employed to select meaningful features from individual and hybrid feature sets. Finally, an ensemble classifier based on majority voting has been proposed, which classifies colon biopsy images using the selected features. Linear, RBF, and sigmoid SVM have been employed as base classifiers. The proposed system has been tested on 174 colon biopsy images, and improved performance (=98.85%) has been observed compared to previously reported studies. Additionally, the use of mRMR method has been justified by comparing the

  20. 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.

  1. Atypical appearance of lipomatous tumors on MR images: high signal intensity with fat-suppression STIR sequences.

    PubMed

    Murphy, W D; Hurst, G C; Duerk, J L; Feiglin, D H; Christopher, M; Bellon, E M

    1991-01-01

    Lipomatous tumors generally have signal characteristics that allow them to be diagnosed with great accuracy by means of magnetic resonance imaging. These tumors usually have signal intensities similar to those of subcutaneous fat on both T1- and T2-weighted spin-echo images. Previous reports have not, to the authors' knowledge, described the appearance of lipomatous tumors on images obtained with a short-inversion-time inversion-recovery (STIR) sequence, which can be used to suppress signal from fat. Three lipomatous tumors (two liposarcomas and one lipoma) with signal characteristics unlike those of normal subcutaneous fat at all pulse sequences are presented.

  2. 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.

  3. 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

  4. 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.

  5. 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.

  6. 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

  7. 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

  8. 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.

  9. 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.

  10. Association of In Vivo [18F]AV-1451 Tau PET Imaging Results With Cortical Atrophy and Symptoms in Typical and Atypical Alzheimer Disease.

    PubMed

    Xia, Chenjie; Makaretz, Sara J; Caso, Christina; McGinnis, Scott; Gomperts, Stephen N; Sepulcre, Jorge; Gomez-Isla, Teresa; Hyman, Bradley T; Schultz, Aaron; Vasdev, Neil; Johnson, Keith A; Dickerson, Bradford C

    2017-04-01

    Previous postmortem studies have long demonstrated that neurofibrillary tangles made of hyperphosphorylated tau proteins are closely associated with Alzheimer disease clinical phenotype and neurodegeneration pattern. Validating these associations in vivo will lead to new diagnostic tools for Alzheimer disease and better understanding of its neurobiology. To examine whether topographical distribution and severity of hyperphosphorylated tau pathologic findings measured by fluorine 18-labeled AV-1451 ([18F]AV-1451) positron emission tomographic (PET) imaging are linked with clinical phenotype and cortical atrophy in patients with Alzheimer disease. This observational case series, conducted from July 1, 2012, to July 30, 2015, in an outpatient referral center for patients with neurodegenerative diseases, included 6 patients: 3 with typical amnesic Alzheimer disease and 3 with atypical variants (posterior cortical atrophy, logopenic variant primary progressive aphasia, and corticobasal syndrome). Patients underwent [18F]AV-1451 PET imaging to measure tau burden, carbon 11-labeled Pittsburgh Compound B ([11C]PiB) PET imaging to measure amyloid burden, and structural magnetic resonance imaging to measure cortical thickness. Seventy-seven age-matched controls with normal cognitive function also underwent structural magnetic resonance imaging but not tau or amyloid PET imaging. Tau burden, amyloid burden, and cortical thickness. In all 6 patients (3 women and 3 men; mean age 61.8 years), the underlying clinical phenotype was associated with the regional distribution of the [18F]AV-1451 signal. Furthermore, within 68 cortical regions of interest measured from each patient, the magnitude of cortical atrophy was strongly correlated with the magnitude of [18F]AV-1451 binding (3 patients with amnesic Alzheimer disease, r = -0.82; P < .001; r = -0.70; P < .001; r = -0.58; P < .001; and 3 patients with nonamnesic Alzheimer disease, r = -0.51; P

  11. 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%.

  12. Manifold-based feature point matching for multi-modal image registration.

    PubMed

    Hu, Liang; Wang, Manning; Song, Zhijian

    2013-03-01

    Images captured using different modalities usually have significant variations in their intensities, which makes it difficult to reveal their internal structural similarities and achieve accurate registration. Most conventional feature-based image registration techniques are fast and efficient, but they cannot be used directly for the registration of multi-modal images because of these intensity variations. This paper introduces the theory of manifold learning to transform the original images into mono-modal modalities, which is a feature-based method that is applicable to multi-modal image registration. Subsequently, scale-invariant feature transform is used to detect highly distinctive local descriptors and matches between corresponding images, and a point-based registration is executed. The algorithm was tested with T1- and T2-weighted magnetic resonance (MR) images obtained from BrainWeb. Both qualitative and quantitative evaluations of the method were performed and the results compared with those produced previously. The experiments showed that feature point matching after manifold learning achieved more accurate results than did the similarity measure for multi-modal image registration. This study provides a new manifold-based feature point matching method for multi-modal medical image registration, especially for MR images. The proposed method performs better than do conventional intensity-based techniques in terms of its registration accuracy and is suitable for clinical procedures. Copyright © 2012 John Wiley & Sons, Ltd.

  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. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

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

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2015-09-01

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

  15. 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

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

    PubMed

    Tan, Maxine; Pu, Jiantao; Zheng, Bin

    2014-08-01

    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. 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. 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 results of the ANNs optimized

  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. MR imaging features of foot involvement in ankylosing spondylitis.

    PubMed

    Erdem, C Zuhal; Sarikaya, Selda; Erdem, L Oktay; Ozdolap, Senay; Gundogdu, Sadi

    2005-01-01

    To determine alterations of the soft tissue, tendon, cartilage, joint space, and bone of the foot using magnetic resonance (MR) imaging in ankylosing spondylitis (AS) patients. Clinical and MR examination of the foot was performed in 23 AS patients (46 feet). Ten asymptomatic volunteers (20 feet) were studied on MR imaging, as a control group. MR imaging protocol included; T1-weighted spin-echo, T2-weighted fast-field echo (FFE) and fat-suppressed short tau inversion recovery (STIR) sequences in sagittal, sagittal oblique, and coronal planes using a head coil. Specifically, we examined: bone erosions, tendinitis (acute and chronic), para-articular enthesophyte, joint effusion, plantar fasciitis, joint space narrowing, soft tissue edema, bone marrow edema, enthesopathy in the Achilles tendon and plantar fascia attachment, subchondral signal intensity abnormalities (edema and sclerosis), tenosynovitis, retrocalcaneal bursitis, subchondral cysts, subchondral fissures, and bony ankylosis. Midfoot, hindfoot, and ankle were included in examined anatomic regions. Clinical signs and symptoms (pain and swelling) due to foot involvement were present in 3 (13%) of the patients while frequency of involvement was 21 (91%) with MR imaging assessment. The MR imaging findings were bone erosions (65%), Achilles tendinitis (acute and chronic) (61%), para-articular enthesophyte (48%), joint effusion (43%), plantar fasciitis (40%), joint space narrowing (40%), subchondral sclerosis (35%), soft tissue edema (30%), bone marrow edema (30%), enthesopathy of the Achilles attachment (30%), subchondral edema (26%), enthesopathy in the plantar fascia attachment (22%), retrocalcaneal bursitis (22%), subchondral cysts (17%), subchondral fissures (17%), tendinitis and enthesopathy of the plantar ligament (13%), and bony ankylosis (9%). The most common involved anatomical region was the hindfoot (83%) following by midfoot (69% ) and ankle (22%). In our experience, MR imaging may detect

  19. 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

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

    NASA Astrophysics Data System (ADS)

    Lim, Joohyun; Paik, Joonki

    2011-11-01

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

  1. 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.

  2. 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

  3. Method for feature analysis in medical imaging. Executive Summary

    SciTech Connect

    McNeil, B.J.

    1990-11-01

    The study was designed to improve the ability of radiologists to detect malignacies and differentiate malignant from benign breast disease in women having mammograms. To this end, a list of perceptual features was developed and the importance of each in the daignosis of patients having a mammogram, a diaphanogram, or both was quantitated. Two decision aids were developed one to assist the readers in assessing the feature numerically and second to assist in merging assessments into diagnostic probability. The enhancement effect of these aids was found to increase in size with increasing difficulty of the test cases, reaching a very substantial level for the most difficult cases. Diaphanography used adjunctively with mammography increased the accuracy of mammography alone only in the enhanced condition, but not to a clinically significant extent.

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

    PubMed

    Zamir, Amir Roshan; Shah, Mubarak

    2014-08-01

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

  5. 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.

  6. Comparative study of texture features in OCT images at different scales for human breast tissue classification.

    PubMed

    Yu Gan; Xinwen Yao; Chang, Ernest; Bin Amir, Syed; Hibshoosh, Hanina; Feldman, Sheldon; Hendon, Christine P

    2016-08-01

    Breast cancer is the second leading cause of death in women in the United States due to cancer. Early detection of breast cancerous regions will aid the diagnosis, staging, and treatment of breast cancer. Optical coherence tomography (OCT), a non-invasive imaging modality with high resolution, has been widely used to visualize various tissue types within the human breast and has demonstrated great potential for assessing tumor margins. Imaging large resected samples with a fast imaging speed can be accomplished by under-sampling in the spatial domain, resulting in a large image scale. However, it is unclear whether there is an impact on the ability to classify tissue types based on the selected imaging scale. Our objective is to evaluate how the scale at which the images are acquired impacts texture features and the accuracy of an automated classification algorithm. To this end, we present a comparative study of texture features in OCT images at two image scales for human breast tissue classification. Texture features and attenuation coefficients were inputs to a statistical classification model, relevance vector machine. The automated classification results from the two image scales were compared. We found that more informative tissue features are preserved in small image scale and accordingly, small image scale leads to more accurate tissue type classification.

  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. 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

  9. Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images.

    PubMed

    Zhang, Lefei; Zhang, Qian; Du, Bo; Huang, Xin; Tang, Yuan Yan; Tao, Dacheng

    2016-09-12

    In hyperspectral remote sensing data mining, it is important to take into account of both spectral and spatial information, such as the spectral signature, texture feature, and morphological property, to improve the performances, e.g., the image classification accuracy. In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier. However, multiple features from various domains definitely have different physical meanings and statistical properties, and thus such concatenation has not efficiently explore the complementary properties among different features, which should benefit for boost the feature discriminability. Furthermore, it is also difficult to interpret the transformed results of the concatenated vector. Consequently, finding a physically meaningful consensus low dimensional feature representation of original multiple features is still a challenging task. In order to address these issues, we propose a novel feature learning framework, i.e., the simultaneous spectral-spatial feature selection and extraction algorithm, for hyperspectral images spectral-spatial feature representation and classification. Specifically, the proposed method learns a latent low dimensional subspace by projecting the spectral-spatial feature into a common feature space, where the complementary information has been effectively exploited, and simultaneously, only the most significant original features have been transformed. Encouraging experimental results on three public available hyperspectral remote sensing datasets confirm that our proposed method is effective and efficient.

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

    NASA Technical Reports Server (NTRS)

    Welch, R.; Ehlers, Manfred

    1988-01-01