49 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.IngenierÃa de Sistemas Audiovisuale
Skin lesion image segmentation using Delaunay Triangulation for melanoma detection
Developing automatic diagnostic tools for the early detection of skin cancer
lesions in dermoscopic images can help to reduce melanoma-induced mortal-
ity. Image segmentation is a key step in the automated skin lesion diagnosis
pipeline. In this paper, a fast and fully-automatic algorithm for skin lesion
segmentation in dermoscopic images is presented. Delaunay Triangulation is
used to extract a binary mask of the lesion region, without the need of any
training stage. A quantitative experimental evaluation has been conducted
on a publicly available database, by taking into account six well-known state-
of-the-art segmentation methods for comparison. The results of the experi-
mental analysis demonstrate that the proposed approach is highly accurate
when dealing with benign lesions, while the segmentation accuracy signi-
cantly decreases when melanoma images are processed. This behavior led us
to consider geometrical and color features extracted from the binary masks
generated by our algorithm for classication, achieving promising results for
melanoma detection