Limits...
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.

Show MeSH

Related in: MedlinePlus

Median filtering: (a) original image, (b) result after median filtering.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig1: Median filtering: (a) original image, (b) result after median filtering.

Mentions: Dermatologists can achieve early detection of skin tumours by studying the medical history of the patient, and also by examining the edge, shape, texture and colour of the lesion. Before such an examination, it is necessary to start by pre-processing and segmenting the skin tumour image. Technical difficulties in image segmentation include variations of brightness, the presence of artefacts (e.g. hair) and variability of edges. The idea is that if there is a transaction on edge detection of a source noised image, we can locate other additional edges due to the presence of noise. Therefore, filtering the noised image is necessary. In our system, we applied median filtering to minimize the influence of small structures (like thin hairs) and to isolate islands of pixels (like small air bubbles) in the segmentation result. For images including thick hairs with colour hue similar to that of the lesion which was not removable by the median filter (figure 1(b)), a specific hair removal technique called DullRazor [3] is applied. The last pre-processing step in our system is the application of the Karhunen–Loève transform, which enhances the edge, making easier extraction of the lesion from the surrounding skin.


Extraction of specific parameters for skin tumour classification.

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

Median filtering: (a) original image, (b) result after median filtering.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig1: Median filtering: (a) original image, (b) result after median filtering.
Mentions: Dermatologists can achieve early detection of skin tumours by studying the medical history of the patient, and also by examining the edge, shape, texture and colour of the lesion. Before such an examination, it is necessary to start by pre-processing and segmenting the skin tumour image. Technical difficulties in image segmentation include variations of brightness, the presence of artefacts (e.g. hair) and variability of edges. The idea is that if there is a transaction on edge detection of a source noised image, we can locate other additional edges due to the presence of noise. Therefore, filtering the noised image is necessary. In our system, we applied median filtering to minimize the influence of small structures (like thin hairs) and to isolate islands of pixels (like small air bubbles) in the segmentation result. For images including thick hairs with colour hue similar to that of the lesion which was not removable by the median filter (figure 1(b)), a specific hair removal technique called DullRazor [3] is applied. The last pre-processing step in our system is the application of the Karhunen–Loève transform, which enhances the edge, making easier extraction of the lesion from the surrounding skin.

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