174 research outputs found
Computer aided diagnosis system using dermatoscopical image
Computer Aided Diagnosis (CAD) systems for melanoma detection aim to mirror the expert
dermatologist decision when watching a dermoscopic or clinical image. Computer Vision
techniques, which can be based on expert knowledge or not, are used to characterize the
lesion image. This information is delivered to a machine learning algorithm, which gives a
diagnosis suggestion as an output.
This research is included into this field, and addresses the objective of implementing a
complete CAD system using ‘state of the art’ descriptors and dermoscopy images as input.
Some of them are based on expert knowledge and others are typical in a wide variety of
problems. Images are initially transformed into oRGB, a perceptual color space, looking for
both enhancing the information that images provide and giving human perception to machine
algorithms. Feature selection is also performed to find features that really contribute to
discriminate between benign and malignant pigmented skin lesions (PSL). The problem of
robust model fitting versus statistically significant system evaluation is critical when working
with small datasets, which is indeed the case. This topic is not generally considered in works
related to PSLs. Consequently, a method that optimizes the compromise between these two
goals is proposed, giving non-overfitted models and statistically significant measures of
performance. In this manner, different systems can be compared in a fairer way. A database
which enjoys wide international acceptance among dermatologists is used for the
experiments.Ingeniería de Sistemas Audiovisuale
Computer Aided Diagnostic Support System for Skin cancer: Review of techniques and algorithms
Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique’s performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided
Computer aided diagnostic support system for skin cancer: A review of techniques and algorithms
Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique's performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided. © 2013 Ammara Masood and Adel Ali Al-Jumaily
Automatic Detection of Blue-White Veil and Related Structures in Dermoscopy Images
Dermoscopy is a non-invasive skin imaging technique, which permits
visualization of features of pigmented melanocytic neoplasms that are not
discernable by examination with the naked eye. One of the most important
features for the diagnosis of melanoma in dermoscopy images is the blue-white
veil (irregular, structureless areas of confluent blue pigmentation with an
overlying white "ground-glass" film). In this article, we present a machine
learning approach to the detection of blue-white veil and related structures in
dermoscopy images. The method involves contextual pixel classification using a
decision tree classifier. The percentage of blue-white areas detected in a
lesion combined with a simple shape descriptor yielded a sensitivity of 69.35%
and a specificity of 89.97% on a set of 545 dermoscopy images. The sensitivity
rises to 78.20% for detection of blue veil in those cases where it is a primary
feature for melanoma recognition
Measuring asymmetries of skin lesions
Since 1994, a clinical study has been established to digitize melanocytic lesions from patients who are referred to the Colored Pigment Lesion Clinic in the University of British Columbia. In the past, we have been using circularity as the main feature to reflect the asymmetrical aspect of skin lesions. However, its significance often depends on the accuracy of image segmentation while the borders of many lesions are often fuzzy and irregular. In this paper, we investigate how to use symmetry distance (SD) to improve the measurement of the asymmetries of skin lesions. Two SDs, including the basic SD and the fuzzy SD, and the simple circularity are calculated based on the new set of color images which are digitized under the controlled environment.published_or_final_versio
An Efficient Block-Based Algorithm for Hair Removal in Dermoscopic Images
Hair occlusion in dermoscopy images affects the diagnostic operation of the skin lesion. Segmentation and classification of skin lesions are two major steps of the diagnostic operation required by Dermatologists. We propose a new algorithm for hair removal in dermoscopy images that includes two main stages: hair detection and inpainting. In hair detection, a morphological bottom-hat operation is implemented on Y-channel image of YIQ color space followed by a binarization operation. In inpainting, the repaired Y-channel is partitioned into 256 nonoverlapped blocks and for each block, white pixels are replaced by locating the highest peak of using a histogram function and a morphological close operation. Our proposed algorithm reports a true positive rate (sensitivity) of 97.36%, a false positive rate (fall-out) of 4.25%, and a true negative rate (specificity) of 95.75%. The diagnostic accuracy achieved is recorded at a high level of 95.78%
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