Malignant melanoma is the most hazardous type of human skin cancer and its incidence has been rapidly increasing. Early detection of malignant melanoma in dermoscopy images is very important and critical, since its detection in the early stage can be helpful to cure it. Computer Aided Diagnosis systems can be very helpful to facilitate the early detection of cancers for dermatologists. In this paper, we present a novel method for the detection of melanoma skin cancer. To detect the hair and several noises from images, pre-processing step is carried out by applying a bank of directional filters. And therefore, Image inpainting method is implemented to fill in the unknown regions. Fuzzy C-Means and Markov Random Field methods are used to delineate the border of the lesion area in the images. The method was evaluated on a dataset of 200 dermoscopic images, and superior results were produced.

One of the most important structures in dermoscopy images is the pigment network, which is also one of the most challenging and fundamental task for dermatologists in early detection of melanoma. This paper presents an automatic system to detect pigment network from dermoscopy images. The design of the proposed algorithm consists of four stages. First, a pre-processing algorithm is carried out in order to remove the noise and improve the quality of the image. Second, a bank of directional filters and morphological connected component analysis are applied to detect the pigment networks. Third, features are extracted from the detected image, which can be used in the subsequent stage. Fourth, the classification process is performed by applying feed-forward neural network, in order to classify the region as either normal or abnormal skin. The method was tested on a dataset of 200 dermoscopy images

The Holling type II harvesting term has the property that it is small for small population values, but grows monotonically with population growth, eventually saturating, at a constant value for very large populations. We consider here a population evolving according to a logistic rate, but harvested (predated) subject to a Holling type II harvesting term that varies slowly with time, possibly due to slow environmental variation. Application of a multitiming method gives us an approximation to the population at any time in two cases- survival to a slowly varying limit, and extinguishment to zero. The situation where there is a transition from survival to extinction is also analyzed, using a matched expansions approach. A uniformly valid approximate expression for the population, valid for all times is obtained. These results are shown to agree well with the results of numerical calculations.