Automatic Skin Lesion Segmentation via Iterative Stochastic Region Merging

Wong, Alexander; Scharcanski, Jacob; Fieguth, Paul

doi:10.1109/titb.2011.2157829

Cited by 119 publications

(78 citation statements)

References 18 publications

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“…There is an abundance of literature that details a wide range of image segmentation approaches and methodologies including, but not limited to, methods employing histogram thresholding [10,11], clustering [12,13], active contours [14,15], edge detection [16,17], graph theory [18], and probabilistic modeling [19,20]. Successful application of these methods, either individually or in combination, is expected to achieve optimum segmentation accuracy while maintaining the robustness to noise [21,22].…”

Section: Related Workmentioning

confidence: 99%

An SVM Framework for Malignant Melanoma Detection Based on Optimized HOG Features

Bakheet

2017

Computation

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Early detection of skin cancer through improved techniques and innovative technologies has the greatest potential for significantly reducing both morbidity and mortality associated with this disease. In this paper, an effective framework of a CAD (Computer-Aided Diagnosis) system for melanoma skin cancer is developed mainly by application of an SVM (Support Vector Machine) model on an optimized set of HOG (Histogram of Oriented Gradient) based descriptors of skin lesions. Experimental results obtained by applying the presented methodology on a large, publicly accessible dataset of dermoscopy images demonstrate that the proposed framework is a strong contender for the state-of-the-art alternatives by achieving high levels of sensitivity, specificity, and accuracy (98.21%, 96.43% and 97.32%, respectively), without sacrificing computational soundness.

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Section: Related Workmentioning

confidence: 99%

An SVM Framework for Malignant Melanoma Detection Based on Optimized HOG Features

Bakheet

2017

Computation

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“…Several technological developments rely on the acquisition and processing of images and videos to extract and enhance information (Scharcanski et al, 1993;Cavalcanti et al, 2010), in areas ranging from engineering Dodson, 1966, 2000;Scharcanski, 2005) and medicine (Wong et al, 2011) to biometric authentication (Behaine and Scharcanski, 2012;Verdoolaege et al, 2014). In particular, the growing processing capability and the improvement of the camera quality of consumer electronic devices (Google, 2016), such as smartphones and laptops, allow to obtain a wide range of vision-based measurements in a variety of areas, such as monitoring food intake, assisting the visual impaired, biometric user authentication, and in several other medicine and engineering applications.…”

Section: Reliable Measurements Via Computer Visionmentioning

confidence: 99%

“…Early diagnosis is very important, since melanoma can be cured with a simple excision if detected early. Recently, macroscopy (i.e., skin lesion imaging with standard cameras) has proved valuable in evaluating morphological structures in pigmented lesions (Cavalcanti et al, 2010Wong et al, 2011;Scharcanski and Celebi, 2014). Although computerized techniques are not intended to provide a definitive diagnosis, these imaging technologies are accessible and can be useful in early melanoma detection (especially for patients with multiple atypical nevi) and can serve as an adjunct to physicians.…”

Section: Reliable Measurements Via Computer Visionmentioning

confidence: 99%

Bringing Vision-Based Measurements into our Daily Life: A Grand Challenge for Computer Vision Systems

Scharcanski

2016

Front. ICT

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Bringing computer vision into our daily life has been challenging researchers in industry and in academia over the past decades. However, the continuous development of cameras and computing systems turned computer vision-based measurements into a viable option, allowing new solutions to known problems. In this context, computer vision is a generic tool that can be used to measure and monitor phenomena in wide range of fields. The idea of using vision-based measurements is appealing, since these measurements can be fast and easy to obtain and handle by humans. On the other hand, these vision-based measurements need to be at least as precise and accurate as those obtained by more traditional measuring systems. This article discusses the perspectives and challenges of introducing vision-based measurements into our daily life.

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“…There are different methods in the literature presenting approaches to segment pigmented skin lesions, with their effectiveness already confirmed [1]. However, most skin lesion segmentation methods generate a deterministic skin lesion rim, even when the lesion is affecting some of the surrounding areas with less intensity or has retracted beneath the skin.…”

Section: Motivation To Segment Skin Lesion Regions Into Three Categoriesmentioning

confidence: 99%

Unsupervised Segmentation and Categorization of Skin Lesions Using Adaptative Thresholds and Stochastic Features

Bernart¹,

Zortea²,

Scharcanski³

et al. 2015

Vis. Letters

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This work presents a novel unsupervised method to segment skin lesions in macroscopic images, grouping the pixels into three disjoint categories, namely 'skin lesion', 'suspicious region' and 'healthy skin'. These skin region categories are obtained by analyzing the agreement of adaptative thresholds applied to the different skin image color channels. In the sequence we use stochastic texture features to refine the suspicious regions. Our preliminary results are promising, and suggest that skin lesions can be segmented successfully with the proposed approach. Also, 'suspicious regions' are identified correctly, where it is uncertain if they belong to skin lesions or to the surrounding healthy skin. Motivation to Segment Skin Lesion Regions into Three CategoriesThere are different methods in the literature presenting approaches to segment pigmented skin lesions, with their effectiveness already confirmed [1]. However, most skin lesion segmentation methods generate a deterministic skin lesion rim, even when the lesion is affecting some of the surrounding areas with less intensity or has retracted beneath the skin. The proposed method tends to detect accurately 'suspicious regions' and refine their detection using stochastic features. Proposed Skin Lesion Segmentation ApproachThe three RGB color channels are used to obtain an initial estimate of the three types of skin regions, and then the initial segmentation is refined using stochastic textures as described in Section 3. We propose segmenting independently each of the RGB color channels using the Otsu's thresholding method. When analyzing the color information in the RGB color space, the lesion area is assumed to be darker than the background skin. For a given pixel, when all the three thresholded channels agree with the pixel categorization as 'healthy skin' or 'skin lesion', we assign it to the 'healthy skin' or 'skin lesion' categories. However, when there is no unanimous agreement among the three thresholded channels, the pixel is assigned to the 'suspicious region' category. More sophisticated algorithms could be used for the initial segmentation. Skin Lesion Segmentation RefinementSince pigmented skin lesions have no specific shape, color or texture, we consider the 'healthy skin' and 'skin lesion' as having different stochastic textures. In order to qualify these different stochastic textures within a skin lesion image, we assume the gradient distribution in these different skin regions as represented by asymmetric distributions (e.g. Gamma or Rayleigh), and use stochastic texture features to measure how much they deviate from randomness [2] (e.g. the regions within a skin lesion tend to be more clustered and less random than the surrounding healthy skin, which tends to be less clustered and more random). Also, describing the local gradient magnitudes by an asymmetric distribution facilitates the discrimination between the stochastic textures inside and outside the pigmented 'skin lesions' [3]. In our approach, the discrimination between the 'skin...

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Automatic Skin Lesion Segmentation via Iterative Stochastic Region Merging

Cited by 119 publications

References 18 publications

An SVM Framework for Malignant Melanoma Detection Based on Optimized HOG Features

An SVM Framework for Malignant Melanoma Detection Based on Optimized HOG Features

Bringing Vision-Based Measurements into our Daily Life: A Grand Challenge for Computer Vision Systems

Unsupervised Segmentation and Categorization of Skin Lesions Using Adaptative Thresholds and Stochastic Features

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