A colour image processing method for melanoma detection

Colot, Olivier; Devinoy, R.; Sombo, A.; Brucq, Denis De

doi:10.1007/bfb0056241

Cited by 19 publications

(10 citation statements)

References 5 publications

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“…In fact, the experimental result presented in Fig. 6(d) is quite consistent with the visualized colour distribution in the objects, which makes it possible to take an accurate measurement of the cells volumes [12].…”

Section: Resultssupporting

confidence: 70%

A New Method for the Estimation of Mass Functions in the Dempster–Shafer’s Evidence Theory: Application to Colour Image Segmentation

Chaabane

Sayadi

Fnaiech

et al. 2010

Circuits Syst Signal Process

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In this paper, the problem of colour image segmentation is addressed using the Dempster-Shafer (DS) theory. Examples are provided showing that this theory is able to take into account a large variety of special situations that occur and which are not well solved using classical approaches. Modelling both uncertainty and imprecision, and computing the conflict between images and introducing a priori information are the main features of this theory. Consequently, the performance of such a segmentation scheme is largely conditioned by the appropriate estimation of mass functions in the DS evidence theory. In this paper, a new method of automatically determining the mass function for colour-image segmentation problems is presented. The mass function of each pixel is determined by applying possibilistic c-means (PCM) clustering to the grey levels of the three primitive colours. A reliability criterion, associ- Circuits Syst Signal Process (2011) 30: 55-71 ated with each pixel and the mass functions of its neighbouring pixels, is used into a fuzzy based reasoning system in order to decide on the appropriate segmentation. Experimental segmentation results on medical and textured colour images highlight the effectiveness of the proposed method.

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Section: Resultssupporting

confidence: 70%

A New Method for the Estimation of Mass Functions in the Dempster–Shafer’s Evidence Theory: Application to Colour Image Segmentation

Chaabane

Sayadi

Fnaiech

et al. 2010

Circuits Syst Signal Process

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show abstract

“…An accurate segmentation of skin images can help the diagnosis to define well the region of the cancer. To obtain the information on lesion area, it is important to run a segmentation process to extract the pixels belonging to the lesion on skin image [24]. Therefore, in this step, the lesion is segmented from its surrounding skin.…”

Section: B Segmentationmentioning

confidence: 99%

Review on automatic early skin cancer detection

Hoshyar

Al-Jumaily

Sulaiman

2011

2011 International Conference on Computer Science and Service System (CSSS)

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Skin cancer is increasing in different countries especially in Australia. Early detection of skin cancer can treat melanoma successfully, therefore, curability and survival depends directly on removing melanoma in its early stages. Since clinical observations face to different fault for melanoma detection, the automatic diagnosis can help to increase the accuracy of detection. Reviewing the research have done in skin cancer detection is the ultimate aim of this paper and to provide an overview on automatic detection of skin cancer. This paper presents the literature on automatic skin cancer detection and also describes the different steps of such process based on the implantation capabilities for such research.

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“…Colored image processing methods have been introduced for detecting the melanoma [3] which focused on non-constant visual information of skin lesions. Neural network diagnosis of skin lesion has been applied by classifying extracted features from digitized dermoscopy images of lesions [4] [5].…”

Section: Introductionmentioning

confidence: 99%

Feasibility Study of Optical Spectroscopy as a Medical Tool for Diagnosis of Skin Lesions

Safi¹,

Ziauddin²,

Horsch³

et al. 2016

ijacsa

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Abstract-Skin cancer is one of the most frequently encountered types of cancer in the Western world. According to the Skin Cancer Foundation Statistics, one in every five Americans develops skin cancer during his/her lifetime. Today, the incurability of advanced cutaneous melanoma raises the importance of its early detection. Since the differentiation of early melanoma from other pigmented skin lesions is not a trivial task, even for experienced dermatologists, computer aided diagnosis could become an important tool for reducing the mortality rate of this highly malignant cancer type.In this paper, a computer aided diagnosis system based on machine learning is proposed in order to support the clinical use of optical spectroscopy for skin lesions quantification and classification. The focuses is on a feasibility study of optical spectroscopy as a medical tool for diagnosis. To this end, data acquisition protocols for optical spectroscopy are defined and detailed analysis of feature vectors is performed. Different techniques for supervised and unsupervised learning are explored on clinical data, collected from patients with malignant and benign skin lesions.

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A colour image processing method for melanoma detection

Cited by 19 publications

References 5 publications

A New Method for the Estimation of Mass Functions in the Dempster–Shafer’s Evidence Theory: Application to Colour Image Segmentation

A New Method for the Estimation of Mass Functions in the Dempster–Shafer’s Evidence Theory: Application to Colour Image Segmentation

Review on automatic early skin cancer detection

Feasibility Study of Optical Spectroscopy as a Medical Tool for Diagnosis of Skin Lesions

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