A Prelimary Approach for the Automated Recognition of Malignant Melanoma

Abstract

In this work, we are motivated by the desire to classify skin lesions as malignants or benigns from color photographic slides of the lesions. Thus, we use color images of skin lesions, image processing techniques and artificial neural network classifier to distinguish melanoma from benign pigmented lesions. As the first step of the data set analysis, a preprocessing sequence is implemented to remove noise and undesired structures from the color image. Second, an automated segmentation approach localizes suspicious lesion regions by region growing after a preliminary step based on fuzzy sets. Then, we rely on quantitative image analysis to measure a series of candidate attributes hoped to contain enough information to differentiate melanomas from benign lesions. At last, the selected features are supplied to an artificial neural network for classification of tumor lesion as malignant or benign. For a preliminary balanced training/testing set, our approach is able to obtain 79.1% of correct classification of malignant and benign lesions on real skin lesion images.

Is the Fractal Dimension of the Contour-lines a Reliable Tool for Classification of Medical Images?

“…The authors in [6] compare the detection rate of malignant melanoma based on clinical visual investigation of about 65% with their approach based on fractal analysis, which gives significant 79.1% correctness. Moreover, the approach of Klein et al in [2] gives high 97% of correctness in identifying malignant cells, which sounds remarkable compared to the current best tumor marker for pancreatic adenocarcinoma CA19-9 -it has its own sensitivity of 50 -70% and applied jointly with two other tumor markers, the sensitivity is up to 85%.…”

“…higher fractal dimension. Although this fractal approach for describing, classifying and recognition of medical images, as well as following and predicting the patient's condition, sounds reasonable and it is justified many times (see for example [1][2][3][4][5][6]), we have detected many ambiguities which put in question the reliability of the fractal dimension when applied in medical diagnosis or in some other field where natural objects are classified on the basis of the estimated values of the fractal dimension of their 2D contours.…”

“…Our experimentation began when we tried to reproduce closely the results met in several papers for successful application of fractal dimension in diagnosing cancer ( [1][2][3][4][5][6][7]). We decided to work with skin cancer, taking into consideration the previously reported results and the dermatological ABCDE rule, according to which one of the most specific characteristic of melanoma moles is their irregular border.…”

Automatic skin lesion segmentation with optimal colour channel from dermoscopic images

“…The process of this software consists of three steps: (1) identifying the locations of dark hair, (2) replacing the hair pixels using bilinear interpolation, and (3) smoothing the image in the final result using adaptive median filter. Smaller structures and artefacts can be removed using median filter with different window sizes 20,21 . This method produces reasonable results when removing thin hair from images, but when attempting to remove thick hair or a regions with large amount of hair, the software was found to have also some parts removed of the lesion from the image.…”

An Ensemble Template Matching and Content-Based Image Retrieval Scheme Towards Early Stage Detection of Melanoma

“…Computer-based automated tools have been previously proposed, which can be used as a second opinion tool for self-skin examination and advice patients regarding the urgency for a physician visit. Moreover, such systems have been used to address another important liability in the early stage detection of melanoma, which is the risk of diagnostic misinterpretations (Stringa, 1988, Field, 1994, Grant-Kels, et al, 1999, Ming, 2000, Zagrouba, et al, 2004, Zhang, et al, 2010, Abikhair, et al, 2014.…”