Unsupervised segmentation for digital dermoscopic images

Møllersen, Kajsa; Kirchesch, Herbert M.; Schopf, Thomas G.; Godtliebsen, Fred

doi:10.1111/j.1600-0846.2010.00455.x

Cited by 27 publications

(17 citation statements)

References 19 publications

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“…Among other studies, there are those that focused on “colour quantization” [15, 17, 18, 23, 28, 30, 71, 135, 136] and those aimed at “colour segmentation” [132, 137–141]. Colour quantization is aimed at reducing the number of colours [88].…”

Section: Discussionmentioning

confidence: 99%

“…This background image was computed by a moving average operation on each row of image data. Møllersen et al [132] used the illumination of an empty field as a correction filter (white shading correction). In this technique, a “bright-field” image was captured before imaging the lesions by placing a “white surface” that covered camera's entire field of view in the scene.…”

Section: Attenuation Of Shadingmentioning

confidence: 99%

See 1 more Smart Citation

Incorporating Colour Information for Computer-Aided Diagnosis of Melanoma from Dermoscopy Images: A Retrospective Survey and Critical Analysis

Madooei

Drew

2016

International Journal of Biomedical Imaging

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Cutaneous melanoma is the most life-threatening form of skin cancer. Although advanced melanoma is often considered as incurable, if detected and excised early, the prognosis is promising. Today, clinicians use computer vision in an increasing number of applications to aid early detection of melanoma through dermatological image analysis (dermoscopy images, in particular). Colour assessment is essential for the clinical diagnosis of skin cancers. Due to this diagnostic importance, many studies have either focused on or employed colour features as a constituent part of their skin lesion analysis systems. These studies range from using low-level colour features, such as simple statistical measures of colours occurring in the lesion, to availing themselves of high-level semantic features such as the presence of blue-white veil, globules, or colour variegation in the lesion. This paper provides a retrospective survey and critical analysis of contributions in this research direction.

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Section: Discussionmentioning

confidence: 99%

Section: Attenuation Of Shadingmentioning

confidence: 99%

Incorporating Colour Information for Computer-Aided Diagnosis of Melanoma from Dermoscopy Images: A Retrospective Survey and Critical Analysis

Madooei

Drew

2016

International Journal of Biomedical Imaging

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“…Before the main analysis, 90 PSL images were preprocessed, including surface fitting7, hair and artifact removal, and automated separation of the lesion from the background by using conventional algorithms8. Thereafter, 13 features (3 morphological, 3 color histogram, 6 textural, and 1 topological) were extracted from each PSL.…”

Section: Methodsmentioning

confidence: 99%

Differences in the Known Cellular Composition of Benign Pigmented Skin Lesions Reflected in Computer-Aided Image Analysis

et al. 2014

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BackgroundComputer-aided image analysis (CAIA) has been suggested as an effective diagnostic tool for pigmented skin lesions (PSLs), especially melanoma. However, few studies on benign PSLs have been reported.ObjectiveThe purpose of this study was to evaluate benign PSLs with our CAIA software and analyze the differences between the parameters of those lesions.MethodsBy using homegrown CAIA software, we analyzed 3 kinds of PSLs-nevus, lentigo, and seborrheic keratosis. The group of seborrheic keratosis was divided into pigmented seborrheic keratosis, sebolentigine, and hyperkeratotic seborrheic keratosis. The CAIA was used to extract the color, as well as the morphological, textural, and topological features from each image.ResultsIn line with clinical observations, the objective parameters indicated that nevus was dark and round, lentigo was small and bright, and seborrheic keratosis was large and spiky. The surface of nevus showed the highest contrast and correlation. In topological analysis, the concentricity clearly separated melanocytic lesions from seborrheic keratosis. The parameters of pigmented seborrheic keratosis were between those of typical nevus and seborrheic keratosis.ConclusionWe confirmed that definite correlations exist between the subjective differentiation by experts' examination and the objective evaluation by using CAIA. We also found that the morphological differences observed in CAIA were greatly influenced by the composition ratios of keratinocytes and melanocytes, which are already known histopathological characteristics of each PSL.

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“…Thus, hair can obstruct reliable lesion detection and feature extraction, resulting in unsatisfactory classification results. This section introduces an image processing technique to detect and exclude hair from the dermoscopy images as an essential step also seen in [19] – [33] . The result is a clean hair mask which can be used to segment and remove the hair in the image, preparing it for further segmentation and analysis.…”

Section: Dermoscopy Images Analysismentioning

confidence: 99%

Noninvasive Real-Time Automated Skin Lesion Analysis System for Melanoma Early Detection and Prevention

Abuzaghleh

Barkana

Faezipour

2015

IEEE J. Transl. Eng. Health Med.

165

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Melanoma spreads through metastasis, and therefore, it has been proved to be very fatal. Statistical evidence has revealed that the majority of deaths resulting from skin cancer are as a result of melanoma. Further investigations have shown that the survival rates in patients depend on the stage of the cancer; early detection and intervention of melanoma implicate higher chances of cure. Clinical diagnosis and prognosis of melanoma are challenging, since the processes are prone to misdiagnosis and inaccuracies due to doctors’ subjectivity. Malignant melanomas are asymmetrical, have irregular borders, notched edges, and color variations, so analyzing the shape, color, and texture of the skin lesion is important for the early detection and prevention of melanoma. This paper proposes the two major components of a noninvasive real-time automated skin lesion analysis system for the early detection and prevention of melanoma. The first component is a real-time alert to help users prevent skinburn caused by sunlight; a novel equation to compute the time for skin to burn is thereby introduced. The second component is an automated image analysis module, which contains image acquisition, hair detection and exclusion, lesion segmentation, feature extraction, and classification. The proposed system uses PH2 Dermoscopy image database from Pedro Hispano Hospital for the development and testing purposes. The image database contains a total of 200 dermoscopy images of lesions, including benign, atypical, and melanoma cases. The experimental results show that the proposed system is efficient, achieving classification of the benign, atypical, and melanoma images with accuracy of 96.3%, 95.7%, and 97.5%, respectively.

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Unsupervised segmentation for digital dermoscopic images

Abstract: The present segmentation algorithm is fast and intuitive. It gives correct segmentation for most types of skin lesions, but fails when the lesion is brighter than the surrounding skin.

Cited by 27 publications

References 19 publications

Incorporating Colour Information for Computer-Aided Diagnosis of Melanoma from Dermoscopy Images: A Retrospective Survey and Critical Analysis

Incorporating Colour Information for Computer-Aided Diagnosis of Melanoma from Dermoscopy Images: A Retrospective Survey and Critical Analysis

Differences in the Known Cellular Composition of Benign Pigmented Skin Lesions Reflected in Computer-Aided Image Analysis

Noninvasive Real-Time Automated Skin Lesion Analysis System for Melanoma Early Detection and Prevention

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