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The accurate detection of the border surrounding the apical lesions observed in cone-beam Computed Tomography (CT) images is very important for the accurate calculation of the features of these lesions. This study describes a semi-automatic hybrid segmentation method that will be used to determine the limits of apical lesions observed in three-dimensional (3D) KIBT images. The apical cyst and tumor...
Lesion localization is the first and most important step in the development of a melanoma detection system. Skin lesions show distinct color variations as they evolve from benign to malignant one. The use of different color spaces can provide the best discriminative information of lesions embedded in a particular color channel and used in efficient segmentation of a digital image. A simple and efficient...
An ensemble method based on supervised learning for segmenting the retinal vessels in color fundus images is proposed on the basis of previous work of Zhu et al. For each pixel, a 36 dimensional feature vector is extracted, including local features, morphological transformation with multi-scale and multi-orientation, and divergence of vector field which is firstly used to extract feature of retinal...
The use of computational techniques in the processing of histopathological images allows the study of the structural organization of tissues and their pathological changes. The overall objective of this work includes the proposal, the implementation and the evaluation of a methodology for the analysis of cervical intraepithelial neoplasia (CIN) from histopathological images. For this pourpose, a pipeline...
Acne is chronic disorder of the pilosebaceous units with excess sebum production, follicular epidermal hyper proliferation, inflammation and P acnes activity. It affects 85% of adolescents at some time during their lives. Dermatologists use manual methods such as direct visual assessment and ordinary flash photography to assess the acne. These methods are very time consuming and tedious. To address...
Melanoma can be cured if it is detected early, so early diagnosis is very important in dermatological practice today. Early and non-invasive diagnosis of melanomas can be done by accurate image segmentation of skin lesions. The medical images, while acquisition are generally bound to contain noise. This paper proposes a robust and efficient image segmentation algorithm using LOG edge detector to extract...
Robust tumor activity quantification recently finds application in challenging medical scenarios like early therapy response detection, radiotherapy treatment planning, etc. This paper targets a quantitative comparison of existing state of the art Positron Emission Tomography (PET) volume delineation methodologies. The different methods evaluated include adaptive threshold based, gradient based and...
This work presents a computer-aided detection (CAD) system to aid radiologists in finding sclerotic bone metastases in the spine on CT images. The spine is first segmented using thresholding, region growing and a vertebra template. A watershed algorithm and a merging routine segment potential lesion candidates in each two-dimensional (2-D) axial CT image. Next, overlapping 2-D detections on sequential...
Intensity based classification relies on contrast between tissue types adjacent in feature space and adequate signal compared to image noise. Contrast between brain tissue types in Multiple Sclerosis patients Magnetic Resonance Imaging is reduced due to the presence of lesions which intensity values overlap with healthy tissue, resulting in tissue misclassification. We propose a new, extended classifier...
Diffusion tensor imaging provides rich information about human brain connectivity in vivo, yet most current methods for fiber tractography or tract segmentation do not address white matter pathologies such as multiple sclerosis lesions, which can alter the diffusion tensor characteristics. We study here the effects of MS lesions on estimated diffusion tensors and how they affect the processing of...
Automatic segmentation of white matter hyperintensities (WMH) from T2-Weighted and FLAIR MRI is a common task that needs to be performed in the analysis of many different diseases. A method to segment the WMH is proposed whereby a local intensity model (LIM) of normal tissue is generated. WMH are detected as outliers from this model. The LIM enables an accurate modeling of intensity variations thus...
White matter hyperintensities (WMH) are commonly seen on T2-weighted images in elderly people. They are considered as a potential marker of vascular pathology and have been associated with motor and cognitive deficits. In this paper, non linear diffusion was applied to FLAIR images and combined with precise anatomical knowledge extracted from T1-weighted images to automatically segment WMH. Evaluation...
A telemedicine network with retina cameras and automated quality control, physiological feature location, and lesion / anomaly detection is a low-cost way of achieving broad-based screening for diabetic retinopathy (DR) and other eye diseases. In the process of a routine eye-screening examination, other non-image data is often available which may be useful in automated diagnosis of disease. In this...
Choroidal Neovascularization (CNV) is a severe retinal disease characterized by abnormal growth of blood vessels in the choroidal layer. Current diagnosis of CNV depends mainly on qualitative assessment of a temporal sequence of fundus fluorescein angiography images. Automated segmentation and identification of the CNV lesion types (either occult or classic) is required to reduce the inter-and intra-...
While many computer-aided detection (CADe) systems for CT colonography can detect polypoid lesions at a high sensitivity level, very few are targeted toward detecting flat lesions. Research has shown that flat lesions are more likely to contain carcinoma than polypoid lesions; therefore, it is imperative that they be adequately detected in screenings and examinations. However, current CADe systems...
We present a simple and elegant method to incorporate user input in a template-based segmentation method for diseased organs. The user provides a partial segmentation of the organ of interest, which is used to guide the template towards its target. The user also highlights some elements of the background that should be excluded from the final segmentation. We derive by likelihood maximization a registration...
Having ground truth is critical for evaluating segmentation algorithms and estimating the ground truth from a collection of manual segmentations remains a hard problem. A proper estimation approach should take into account and compensate for the inter-rater variation. In this paper, we conduct an analysis of manual segmentations in order to have a better understanding of the pattern of the variation...
This paper presents a modified region-scalable fitting (RSF) model in [1] and a more efficient narrow band algorithm to perform level set evolution. A distance regularization term is used to maintain the regularity of the level set function, which is necessary for maintaining stable level set evolution and ensuring accurate numerical computation. The computational efficiency of our algorithm is further...
The present article proposes a methodology for simulating breast ultrasound (BUS) images for assessing segmentation algorithms. The main characteristics of the simulated BUS images are: (i) building an echogenicity map containing common tissues found in real breasts as well as their geometries and (ii) simulating the physical process of image acquisition. The validation procedure is based on measuring...
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