Segmentation of Dermatoscopic Images Used for Computer-Aided Diagnosis of Melanoma

Messadi, Mahammed; Bessaid, Abdelhafid; Taleb‐Ahmed, Abdelmalik

doi:10.1142/s0219519410003344

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…In this perspective, the aim of CADx systems is to increase clinician performance by helping in the early identification and localization of potential abnormalities [ 2 ]. Therefore CADx system operates as an automated second opinion or as a double reading system that supports dermatologists in early assessment of skin cancer and in the follow-up of pigmented skin lesions [ 3 ].…”

Section: Overviewmentioning

confidence: 99%

Simple and Accurate Border Detection Algorithm for Melanoma Computer Aided Diagnosis

Guaragnella

Rízzí

2020

Diagnostics

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The interest of the scientific community for computer aided skin lesion analysis and characterization has been increased during the last years for the growing incidence of melanoma among cancerous pathologies. The detection of melanoma in its early stage is essential for prognosis improvement and for guaranteeing a high five-year relative survival rate of patients. The clinical diagnosis of skin lesions is challenging and not trivial since it depends on human vision and physician experience and expertise. Therefore, a computer method that makes an accurate extraction of important details of skin lesion image can assist dermatologists in cancer detection. In particular, the border detection is a critical computer vision issue owing to the wide range of lesion shapes, sizes, colours and skin texture types. In this paper, an automatic and effective pigmented skin lesion segmentation method in dermoscopic image is presented. The proposed procedure is adopted to extract a mask of the lesion region without the adoption of other signal processing procedures for image improvement. A quantitative experimental evaluation has been performed on a publicly available database. The achieved results show the method validity and its high robustness towards irregular boundaries, smooth transition between lesion and skin, noise and artifact presence.

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Section: Overviewmentioning

confidence: 99%

Simple and Accurate Border Detection Algorithm for Melanoma Computer Aided Diagnosis

Guaragnella

Rízzí

2020

Diagnostics

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“…Recent developments have introduced automated systems aiming at discriminating melanomas from other lesions including lesion boundary analysis [15], using features derived from the shape of the lesion boundary and using image surface features to model the surface in homogeneity of skin tumours [16], which is an important hint in early diagnosis of malignant melanomas. With the advent of hand held mobile devices a number of applications have recently been developed for image pre-processing, image feature extraction, and classification of melanomas [17][18][19]. However, the automation supported by the current applications imposes legal implications which prohibit their commercial utilization.…”

Section: Introductionmentioning

confidence: 99%

MARK1 - A Decision Support System for the Early Detection of Malignant Melanoma

Perakis

Bouras

Kostopoulos

et al. 2014

Proceedings of the 4th International Conference on Wireless Mobile Communication and Healthcare - "Transforming Healthcare Thro

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Early stage detection of melanoma (one of the most common cancers today) is of major significance for increasing chances of long term survival of affected patients. Over the last decade there have been developments in skin diagnostics, facilitated by the use of technologies such as Total Body Photography (TBP), which provide a complete record of the skin, and by the development of applications on handheld devices seeking to characterize skin lesions as part of routine selfexamination. Unfortunately, these processes are rather inefficient, inaccurate, and not fully automated, missing also critical components such as the automated ability to compare between two TBP image sets in order to locate essential new and altered skin lesions. Despite some progress and because of its many flaws, the common practice today for early detection is skin self-examination. However, it is important to note that skin selfexamination is usually underestimated by individuals, resulting in poor prognosis. The main objective of the present paper is to present the conceptual architecture of a platform that can address the need for early and accurate detection of skin lesion through a screening solution that will be easily accessible to the general public with the guidance, supervision and inspection of the primary care physician.

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Analysis of Specific Parameters for Skin Tumor Classification

Messadi

MAHMOUDI²,

Bessaid

2022

J. Mech. Med. Biol.

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During the last years, computer vision-based diagnosis systems have been widely used in several hospitals and dermatology clinics, aiming mostly at the early detection of malignant melanoma tumor, which is among the most frequent types of skin cancer, versus other types of nonmalignant cutaneous diseases. They grow in melanocytes, the cells responsible for pigmentation. This type of cancer is increasing rapidly and its related mortality rate is increasing more modestly, and inversely proportional to the tumor’s thickness. The mortality rate can be decreased by earlier detection of suspicious lesions and better prevention. In this work, we are interested in extracting all specific attributes which can be used for computer-aided diagnosis of melanoma. In the first step of the proposed work, we applied the Dull Razor [Lee T et al., Dullrazor: A software approach to hair removal from images, Cancer Control Research, British Columbia Cancer Agency, Vancouver, Canada, Vol. 21, No. 6, pp. 533–543, 1997] technique to images to reduce the influence of small structures, hairs, bubbles, light reflection. In the second step, a new fuzzy level set algorithm is proposed in order to facilitate the medical image segmentation task. It is able to directly evolve from the initial segmentation proposed that uses a spatial fuzzy clustering approach. The controlling parameters of the level set evolution are also estimated from the results of the fuzzy clustering step. This step is essential to characterize the shape of the lesion and also to locate the tumor to be analyzed. In this paper, we have also treated the necessity to extract all the specific attributes used to develop a characterization methodology that enables specialists to take the best possible diagnosis. For this purpose, our proposal relies largely on visual observation of the tumor while dealing with some characteristics as color, texture or form. The method used in this paper is called ABCD. It requires calculating four factors: Asymmetry ([Formula: see text], Border ([Formula: see text], Color ([Formula: see text], and Diversity ([Formula: see text]. Finally, these parameters are used to construct a classification module based on artificial neural network for the recognition of malignant melanoma.

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Segmentation of Dermatoscopic Images Used for Computer-Aided Diagnosis of Melanoma

Cited by 3 publications

References 4 publications

Simple and Accurate Border Detection Algorithm for Melanoma Computer Aided Diagnosis

Simple and Accurate Border Detection Algorithm for Melanoma Computer Aided Diagnosis

MARK1 - A Decision Support System for the Early Detection of Malignant Melanoma

Analysis of Specific Parameters for Skin Tumor Classification

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