92 research outputs found
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
Statistical techniques applied to the automatic diagnosis of dermoscopic images
An image based system implementing a well‐known diagnostic method is disclosed for the automatic detection of melanomas as
support to clinicians. The software procedure is able to recognize automatically the skin lesion within the digital image, measure
morphological and chromatic parameters, carry out a suitable classification for the detection of structural dermoscopic criteria
provided by the 7‐Point Check. Original contribution is referred to advanced statistical techniques, which are introduced at different
stages of the image processing, including the border detection, the extraction of low‐level features and scoring of high order features
(namely dermoscopic criteria). The proposed approach is experimentally tested with reference to a large image set of pigmented
lesions
Approximate Lesion Localization in Dermoscopy Images
Background: Dermoscopy is one of the major imaging modalities used in the
diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty
and subjectivity of human interpretation, automated analysis of dermoscopy
images has become an important research area. Border detection is often the
first step in this analysis. Methods: In this article, we present an
approximate lesion localization method that serves as a preprocessing step for
detecting borders in dermoscopy images. In this method, first the black frame
around the image is removed using an iterative algorithm. The approximate
location of the lesion is then determined using an ensemble of thresholding
algorithms. Results: The method is tested on a set of 428 dermoscopy images.
The localization error is quantified by a metric that uses dermatologist
determined borders as the ground truth. Conclusion: The results demonstrate
that the method presented here achieves both fast and accurate localization of
lesions in dermoscopy images
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%
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
Sector Expansion and Elliptical Modeling of Blue-Gray Ovoids for Basal Cell Carcinoma Discrimination in Dermoscopy Images
Background: Blue-gray ovoids (B-GOs), a critical dermoscopic structure for basal cell carcinoma (BCC), offer an opportunity for automatic detection of BCC. Due to variation in size and color, B-GOs can be easily mistaken for similar structures in benign lesions. Analysis of these structures could afford accurate characterization and automatic recognition of B-GOs, furthering the goal of automatic BCC detection. This study utilizes a novel segmentation method to discriminate B-GOs from their benign mimics.
Methods: Contact dermoscopy images of 68 confirmed BCCs with B-GOs were obtained. Another set of 131 contact dermoscopic images of benign lesions possessing B-GO mimics provided a benign competitive set. A total of 22 B-GO features were analyzed for all structures: 21 color features and one size feature. Regarding segmentation, this study utilized a novel sector-based, non-recursive segmentation method to expand the masks applied to the B-GOs and mimicking structures. Results: Logistic regression analysis determined that blue chromaticity was the best feature for discriminating true B-GOs in BCC from benign, mimicking structures. Discrimination of malignant structures was optimal when the final B-GO border was approximated by a best-fit ellipse. Using this optimal configuration, logistic regression analysis discriminated the expanded and fitted malignant structures from similar benign structures with a classification rate as high as 96.5%.
Conclusions: Experimental results show that color features allow accurate expansion and localization of structures from seed areas. Modeling these structures as ellipses allows high discrimination of B-GOs in BCCs from similar structures in benign images
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