121 research outputs found
Automatic classification of skin lesions using geometrical measurements of adaptive neighborhoods and local binary patterns
ISBN:978-1-4799-8339-1International audienceThis paper introduces a method for characterizing and classifying skin lesions in dermoscopic color images with the goal of detecting which ones are melanoma (cancerous lesions). The images are described by means of the Local Binary Patterns (LBPs) computed on geometrical feature maps of each color component of the image. These maps are extracted from geometrical measurements of the General Adaptive Neighborhoods (GAN) of the pixels. The GAN of a pixel is a region surrounding it and fitting its local image spatial structure. The performance of the proposed texture descriptor has been evaluated by means of an Artificial Neural Network, and it has been compared with the classical LBPs. Experimental results using ROC curves show that the GAN-based method outperforms the classical one and the dermatologists' predictions
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
Analysis of the contour structural irregularity of skin lesions using wavelet decomposition
The boundary irregularity of skin lesions is of clinical significance for the early detection of
malignant melanomas and to distinguish them from other lesions such as benign moles. The
structural components of the contour are of particular importance. To extract the structure from
the contour, wavelet decomposition was used as these components tend to locate in the lower
frequency sub-bands. Lesion contours were modeled as signatures with scale normalization to
give position and frequency resolution invariance. Energy distributions among different wavelet
sub-bands were then analyzed to extract those with significant levels and differences to enable
maximum discrimination.
Based on the coefficients in the significant sub-bands, structural components from the original
contours were modeled, and a set of statistical and geometric irregularity descriptors researched
that were applied at each of the significant sub-bands. The effectiveness of the descriptors was
measured using the Hausdorff distance between sets of data from melanoma and mole contours.
The best descriptor outputs were input to a back projection neural network to construct a
combined classifier system. Experimental results showed that thirteen features from four
sub-bands produced the best discrimination between sets of melanomas and moles, and that a
small training set of nine melanomas and nine moles was optimum
Texture Segmentation by Evidence Gathering
A new approach to texture segmentation is presented which uses Local Binary Pattern data to provide evidence from which pixels can be classified into texture classes. The proposed algorithm, which we contend to be the first use of evidence gathering in the field of texture classification, uses Generalised Hough Transform style R-tables as unique descriptors for each texture class and an accumulator is used to store votes for each texture class. Tests on the Brodatz database and Berkeley Segmentation Dataset have shown that our algorithm provides excellent results; an average of 86.9% was achieved over 50 tests on 27 Brodatz textures compared with 80.3% achieved by segmentation by histogram comparison centred on each pixel. In addition, our results provide noticeably smoother texture boundaries and reduced noise within texture regions. The concept is also a "higher order" texture descriptor, whereby the arrangement of texture elements is used for classification as well as the frequency of occurrence that is featured in standard texture operators. This results in a unique descriptor for each texture class based on the structure of texture elements within the image, which leads to a homogeneous segmentation, in boundary and area, of texture by this new technique
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