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Extraction of specific parameters for skin tumour classification.

Messadi M, Bessaid A, Taleb-Ahmed A - J Med Eng Technol (2009)

Bottom Line: The mortality rate can be decreased by earlier detection of suspicious lesions and better prevention.This method reduces a colour image into an intensity image and approximately segments the image by intensity thresholding.Then, a sequences of transformations is applied to the image to measure a set of attributes (A: asymmetry, B: border, C: colour and D: diameter) which contain sufficient information to differentiate a melanoma from benign lesions.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Engineering Laboratory, Department of Biomedical Electronics, Sciences Engineering Faculty, Abou Bekr Belkaid University, Tlemcen, Algeria. m_messadi@mail.univ-tlemcen.dz

ABSTRACT
In this paper, a methodological approach to the classification of tumour skin lesions in dermoscopy images is presented. Melanomas are the most malignant skin tumours. They grow in melanocytes, the cells responsible for pigmentation. This type of cancer is increasing rapidly; its related mortality rate is increasing more modestly, and inversely proportional to the thickness of the tumour. The mortality rate can be decreased by earlier detection of suspicious lesions and better prevention. Using skin tumour features such as colour, symmetry and border regularity, an attempt is made to determine if the skin tumour is a melanoma or a benign tumour. In this work, we are interested in extracting specific attributes which can be used for computer-aided diagnosis of melanoma, especially among general practitioners. In the first step, we eliminate surrounding hair in order to eliminate the residual noise. In the second step, an automatic segmentation is applied to the image of the skin tumour. This method reduces a colour image into an intensity image and approximately segments the image by intensity thresholding. Then, it refines the segmentation using the image edges, which are used to localize the boundary in that area of the skin. This step is essential to characterize the shape of the lesion and also to locate the tumour for analysis. Then, a sequences of transformations is applied to the image to measure a set of attributes (A: asymmetry, B: border, C: colour and D: diameter) which contain sufficient information to differentiate a melanoma from benign lesions. Finally, the various signs of specific lesion (ABCD) are provided to an artificial neural network to differentiate between malignant tumours and benign lesions.

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Results of the Karhunen–Loeve transform: (a) original image, (b) 1st principal component represents 98.87% of the total variance, (c) 2nd component represents 1.08%; (d) 3rd component represents 0.0036.
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fig3: Results of the Karhunen–Loeve transform: (a) original image, (b) 1st principal component represents 98.87% of the total variance, (c) 2nd component represents 1.08%; (d) 3rd component represents 0.0036.

Mentions: We define a matrix A such that the lines are the eigenvectors of the matrix Cx ordered by the decline in eigenvalues. The KLT of the vector X is defined by the following equation [4]:(3)y=A.(X−mx).Due to the decreasing ordering of the eigenvalues and corresponding eigenvectors, the first principal component will contain the maximum variance. Since most variation occurs at edges between lesion and surrounding skin; the first principal component is a natural choice for segmentation (figure 3).


Extraction of specific parameters for skin tumour classification.

Messadi M, Bessaid A, Taleb-Ahmed A - J Med Eng Technol (2009)

Results of the Karhunen–Loeve transform: (a) original image, (b) 1st principal component represents 98.87% of the total variance, (c) 2nd component represents 1.08%; (d) 3rd component represents 0.0036.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC2683694&req=5

fig3: Results of the Karhunen–Loeve transform: (a) original image, (b) 1st principal component represents 98.87% of the total variance, (c) 2nd component represents 1.08%; (d) 3rd component represents 0.0036.
Mentions: We define a matrix A such that the lines are the eigenvectors of the matrix Cx ordered by the decline in eigenvalues. The KLT of the vector X is defined by the following equation [4]:(3)y=A.(X−mx).Due to the decreasing ordering of the eigenvalues and corresponding eigenvectors, the first principal component will contain the maximum variance. Since most variation occurs at edges between lesion and surrounding skin; the first principal component is a natural choice for segmentation (figure 3).

Bottom Line: The mortality rate can be decreased by earlier detection of suspicious lesions and better prevention.This method reduces a colour image into an intensity image and approximately segments the image by intensity thresholding.Then, a sequences of transformations is applied to the image to measure a set of attributes (A: asymmetry, B: border, C: colour and D: diameter) which contain sufficient information to differentiate a melanoma from benign lesions.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Engineering Laboratory, Department of Biomedical Electronics, Sciences Engineering Faculty, Abou Bekr Belkaid University, Tlemcen, Algeria. m_messadi@mail.univ-tlemcen.dz

ABSTRACT
In this paper, a methodological approach to the classification of tumour skin lesions in dermoscopy images is presented. Melanomas are the most malignant skin tumours. They grow in melanocytes, the cells responsible for pigmentation. This type of cancer is increasing rapidly; its related mortality rate is increasing more modestly, and inversely proportional to the thickness of the tumour. The mortality rate can be decreased by earlier detection of suspicious lesions and better prevention. Using skin tumour features such as colour, symmetry and border regularity, an attempt is made to determine if the skin tumour is a melanoma or a benign tumour. In this work, we are interested in extracting specific attributes which can be used for computer-aided diagnosis of melanoma, especially among general practitioners. In the first step, we eliminate surrounding hair in order to eliminate the residual noise. In the second step, an automatic segmentation is applied to the image of the skin tumour. This method reduces a colour image into an intensity image and approximately segments the image by intensity thresholding. Then, it refines the segmentation using the image edges, which are used to localize the boundary in that area of the skin. This step is essential to characterize the shape of the lesion and also to locate the tumour for analysis. Then, a sequences of transformations is applied to the image to measure a set of attributes (A: asymmetry, B: border, C: colour and D: diameter) which contain sufficient information to differentiate a melanoma from benign lesions. Finally, the various signs of specific lesion (ABCD) are provided to an artificial neural network to differentiate between malignant tumours and benign lesions.

Show MeSH
Related in: MedlinePlus