Skin Lesion Detection using Automatic Neural Network Segmentation

D. Ramya1, G. Sri Lakshmi2 and S. Prithi3 1,2 B.E/ ECE, Prince Shri Venkateshwara Padmavathy Engineering College 3 Asst.Prof /ECE, Prince Shri Venkateshwara Padmavathy Engineering College Abstract- Skin lesion diagnosis is an initial step in detecting skin cancer. Early diagnosis can save the patients. Diagnosis method uses imaging processing technique and artificial intelligence. Challenge in implementing this automated system is locating the skin lesion in digital image. A fundamental step of image processing is image enhancement. The image is subjected to pre-processing for removing the noise and enhancing the image. Brightness Preserving Dynamic Fuzzy Histogram Equalisation is an attractive method to enhance the image considering the local histogram method. This method is employed to provide crisper image by increasing the number of pixels between the interval. Then the image is segmented using Artificial Neural Network. The proposed framework has high accuracy compared to other algorithms.

Keywords- Pre-processing, Image enhancement, neural networking.

I. INTRODUCTION

Malignant melanoma is a type of skin cancer developed from the cells

containing pigment known as melanocytes. This accounts for approximately 75% of death due to skin cancer. The primary cause of melanoma is ultraviolet(UV) light exposure. In early diagnosis, curing rate is 100%. Melanoma may be prevented by avoiding UV light and using sunscreen. It can be treated by surgery. Fuzzy theory is combined with histogram equalisation for image enhancement and algorithm based on Artificial Neural Network is employed for segmentation.

The rest of the paper is organized as follows: section II describes the pre- processing method. Section III is about the centroid calculation. Section IV describes ANN segmentation. Section V shows the results and the final section concludes the paper followed by references.

II. PROCESS OVERVIEW

The pre-processing system ensures the step by step process. The process steps involves dermoscopy, colour space, brightness preserving dynamic fuzzy histogram equalisation, image reconstruction and resizing.

A. Dermoscopy

Dermoscopy is a imaging technique used to examine skin lesion. This process is carried out by placing oil immersion between skin and the optics. Lens of the microscope is placed directly, illuminating sub-surface structures. This provides the details of colour and the texture of skin lesions.

B. Color space

The input image taken from medical image database of patient is of RGB format. This image is converted to Lab color space to sharpen image. CIE L*a*b* defines one colour with three values L*, a* and b*. Lightness (L*) shows whether the light is light or dark. It always ranges from 0-100. The two axes a* and b* tells where the color is placed. The range of a* and b* will never exceeds -100 to 100. Lab space removes artifacts in images from digital cameras.

C. BPDFHE

The new image enhancement algorithm uses Brightness Preserving Dynamic Fuzzy Histogram Equalisation. Fuzzification improves the crisp definition and number of pixels in an interval of an image. The multiplication of the length of an interval and the intensity frequency is fuzzified by a triangular shaped membership function.

D. Image reconstruction and re- sizing

Image reconstruction is done to ensure better processing of an image. The image is subjected to discrete wavelet transformation technique for approximation and detail calculations. Then the image is denoised by applying spatial correlator filter. In correlation, the value of an output pixel is computed as a weighted sun of the neighbourhood pixels. Re-sizing of an image is done to improve the quality of that image.

III. CENTROID CALCULATION

The resized image undergoes calculations like mean and Eigen faces. Then the centroid of the processed image is calculated and Sobel operator is applied to determine horizontal and vertical edges. Gradient magnitude of the image is calculated by border replication of these two images.

IV. ANN SEGMENTATION

Removal of healthy skin from the image to determine the region of interest is done by segmentation. In this proposed system, Artificial Neural Network technique is employed for segmentation. This has input layer, hidden layer and one output layer. Pixel value of the image is given to the input layer. Then train the network with the selected pixels. Use this network to segment the image as foreground and background.

OUTPUT

Learning based on experience and training makes ANN ideal for diagnosis application.

V. RESULTS AND DISCUSSIONS

INPUT

HIDDEN

Fig. 1. Structure of Artificial Neural Network

A morphological structuring element of rectangular shape is set over the skin lesion region of the watershed segmented image. Mesh plot of this image is generated as initial set function.

The Artificial Neural Network is trained to segment the lesion region present in the image by selecting the pixel value manually. Iterations of ANN continues till the lesion is segmented completely. The iterations depend on the size of the lesion region. 260 iterations of the dermoscopic image showed that this novel algorithm outperformed other algorithms.

The mesh plot of the fully segmented image is generated as final set function. The mesh plot draws a wireframe mesh on the image.

In this system, dermoscopic images were collected and they have undergone pre-processing to remove noise and enhance the image. Pre- processed images were segmented by Artificial Neural Network. It segment the image as foreground and background. We used single image and iterated it for 260 times based on the size of lesion region. ANN shows better performance in terms of PSNR(peak to noise signal ratio). The proposed system is more convenient than biopsy technique and also skin removal is not required for diagnosis.

(a) (b)

(c) (d)

(e)

Fig. 2. a) Input image b) BPDFHE c) Filtered image d) Initial level contour e) Final level contour

VI. CONCLUSION

A computer based skin cancer detection is a new discovery. This proves to be a better method using digital image processing technique. Image is pre-processed and enhanced by applying Brightness Preserving Dynamic Fuzzy Histogram Equalisation and spatial correlator filter. It is segmented by Artificial Neural Network method. Accuracy is improved using training algorithm of ANN for this system. Based on this, images are classified as cancerous and non-cancerous.

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