GUI-based Melanoma Diagnosis Tool for Skin Lesions

Abstract

Skin cancer is caused due to irregular growth of cells. There are three major types of skin cancer- Melanoma, Basal, and Squamous cell Carcinoma. Among the three the most dangerous form is Melanoma. If it is left untreated, it leads to the spreading of all over the body including organs. However, the early stage detection of Melanoma is really helpful to cure it. Computer vision can be used for the medical image diagnosis and it has been proved by many prevailing systems. This project focuses on implementing an interface-based computer-aided tool for the early detection of Melanoma skin cancer. The computer-aided tool makes use of non-dermoscopic images of lesions and it automatically extracts affected regions. It checks for Melanoma parameters like Asymmetry, Border, Colour, and Diameter (ABCD) for image segmentation. They are used to identify the Melanoma cancer segment. The simple input requirement and the robustness of its identification allow this Graphical User Interface-based Computer Aided tool to be an efficient tool within the diagnostic process of Melanoma.

Chapter1. Introduction

1.1 Introduction to Skin Cancer

Among all cancers known to us, Skin Cancer is the most common one. When the transformation and insignificant growth of normal cells take place, Cancer is said to have occurred. Tumor is the mass multiplication of cells. Tumor is also known as skin lesions. If malignant cells are present it is concluded as cancerous. This means that they also affect the neighboring tissues. Tumors may also travel through the bloodstream to organs. This process of spreading to other organs is known as metastasis. Tumors take all the nutrients required for the unaffected cells to work. Skin cancers are of three types: Basal Cell Carcinoma, Squamous Cell Carcinoma, and Melanoma. While only about 1% of diagnosed cases of skin cancer are Melanoma, they cause 75% of related deaths. It kills about 10,000 people each year, i.e., nearly one person dies of each Melanoma in every hour. According to World Health Organization currently, between 2 and 3 million non-melanoma skin cancers and 132,000 melanoma skin cancers occur globally each year. The global incidence of Melanoma continues to increase – however, the factors of cause of Melanoma are connected with exposure to the sun and a history of sunburn. These factors are within one’s own responsibility. If the diagnosis and treatment of Melanoma is done in its early stages, it can be cured. If diagnosis becomes late, it can grow deeper into the skin and spread all over the body. The main cause of Melanoma is exposure of skin to ultraviolet radiation.

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Dermoscopy is a non-invasive examination technique based on the use of incident light and oil immersion to make possible the visual examination of subsurface structures of the skin[1]. Though the detection of melanoma using dermoscopy is higher than unaided observation-based detection, its diagnostic accuracy depends on the training of the dermatologist[2]. Even when the expert dermatologist uses the dermoscopy for diagnosis, the accuracy of melanoma diagnosis is estimated to be about 75-84%[3]. The computer-aided diagnostics is very helpful for its accuracy. Computer is not more intelligent than humans but it may be able to extract some information, like color variation, asymmetry, texture features, that may not be perceived by human eyes.

The key steps in a Computer-Aided diagnosis of Melanoma are: image enhancement of a skin lesion, Preprocessing of skin lesion, Segmentation of the skin lesion from the skin region, extraction of features of the lesion blob, and feature classification. The first model is being implemented in Matlab and after with the help of GUI, diagnosis is carried out.

Chapter-2. Literature Survey

Many researchers have been working on the Computer vision approach for skin cancer detection. For segmentation of skin lesions in the input image, existing systems either use manual, semi-automatic, or fully automatic border detection methods. The features to perform skin lesion segmentation used in various papers are shape, colour, texture, and luminance. Many border detection methods are reported in the literature[4,5]. Some of the methods include histogram thresholding[6] global thresholding on optimized colour channels followed by morphological operations[7], and Hybrid thresholding[7]. In [4], the author has introduced an automated Global border-detection method in dermoscopy images based on color-space analysis and global histogram thresholding which exhibits high performance in detecting the borders of melanoma lesions. In [8], the authors have used the technique of dividing the input image into various clinically significant regions using the Euclidean distance transform for the extraction of color and texture features. The ABCD rule of dermoscopy[9], suggests that asymmetry is given the most prominent among the four features of asymmetry, border irregularity, colour, and diameter. A number of studies have been carried out on quantifying asymmetry in skin lesions. In Some techniques, the symmetry feature is calculated based on geometrical measurements on the whole lesion, e.g. symmetric distance and circularity[10].

Chapter3. Work done

3.1 Proposed model in Matlab

The proposed model in Matlab is as follows

• Load Image: In Matlab, a non-dermoscopic image is being loaded. Image may not be enhanced, it may contain noise and unwanted hair.
• Preprocessing: Values of blue channel of image are extracted since blue channel values lead to higher SNR values. Image enhancement and noise removal is also done in preprocessing. Image enhancement means increasing contrast of image by mapping of intensity value to new values. Noise removal is done by various filters such as Wiener filter, N-D filter, Median filter, and Averaging filter. Among all these filters Median filter is used for the extracted blue channel values of image. Now the image is enhanced and noise free.
• Segmentation: It is process of separating affected skin lesions from normal skin. K-means clustering-based image segmentation process is used among all the segmentation processes. It is process of partitioning image into parts or regions. This is based on characteristics of pixels in the image. At first, it is formed into clusters. Clusters mean grouping of data points together which are so similar. K represents number of centriods, which means centre of cluster.
• Post-processing: In this post-processing Morphological filter is used. Morphology is a broad set of image processing operations that process images based on shapes. At first, each pixel is being compared with the neighbor's pixel and it is normalized accordingly. Rule which is used to process the pixels is dilation. In this value of output pixel is maximum value of all the pixel in the neighborhood.

Chapter-4. Summary

Table 1 Quantitative analysis of the different denoising filters

• Images
• Filters
• Signal-to-noise ratio (SNR)

Red Channel Image

1. Wiener filter
2. N-D filter
3. Median filter
4. Averaging filter

• 12.4449
• 10.5746
• 13.9916
• 12.5419

Green Channel Image

1. Wiener filter
2. N-D filter
3. Median filter
4. Averaging filter

• 14.4867
• 11.5672
• 15.6644
• 14.5197

Blue Channel Image

1. Wiener filter
2. N-D filter
3. Median filter
4. Averaging filter

• 15.9593
• 13.0643
• 16.5475
• 15.8369

Grayscale Image

1. Wiener filter
2. N-D filter
3. Median filter
4. Averaging filter

• 13.9013
• 11.3304
• 15.2304
• 14.0110

Results:

• Matlab:
• Image enhancement:
• Noise removal:
• Segmentation using K-means Clustering:
• Morphological Filtering:
• GUI:

Chapter-5. Future work

Future work includes following steps

• Features extraction from the morphologic image and classify it as melanoma or normal skin.
• Implementation of various algorithms for the segmentation and classification and choosing the best one.

Chapter-6 References

1. Jain, S. and Pise, N., 2015. Computer-aided melanoma skin cancer detection using image processing. Procedia Computer Science, 48, pp.735-740.
2. Bafounta ML, Beauchet A, Aegerter P, Saiag P. Is dermoscopy (chemiluminescence microscopy) useful for the diagnosis of melanoma? Results of a meta-analysis using techniques adapted to the evaluation of diagnostic tests. Arch Dermatol,137:13,43–50. 2001.
3. G.Argenziano, H. Soyer, S. Chimenti, R. Talamini, R. Corona, F. Sera, and M. Binder, Dermoscopy of pigmented skin lesions: Results of a consensus meeting via the Internet Journal of the American Academy of Dermatology, vol. 48, pp. 679–693, 2003.
4. R. Garnavi, Computer-aided diagnosis of melanoma, Ph.D. dissertation, University of Melbourne, Australia, 2011
5. M.E. Celebi, H. Iyatomi, G. Schaefer, and W. V. Stoecker, Lesion border detection in dermoscopy images Computerised Medical Imaging and Graphics, vol. 33, no. 2, pp. 148–153, 2009
6. H. Iyatomi, H. Oka, M. Saito, A. Miyake, M. Kimoto, J. Yamagami, S. Kobayashi, A. Tanikawa, M. Hagiwara, K. Ogawa, G. Argenziano, H. P. Soyer, and M. Tanaka, Quantitative assessment of tumour extraction from dermoscopy images and evaluation of computer-based extraction methods for an automatic melanoma diagnostic system Melanoma Research, vol. 16, no. 2, pp. 183–190, 2006
7. R. Garnavi, M. Aldeen, M. E. Celebi, A. Bhuiyan, C. Dolianitis, and G. Varigos, Automatic segmentation of dermoscopy images using histogram thresholding on optimal color channels International Journal of Medicine and Medical Sciences, vol. 1, no. 2, pp. 126–134, 2010
8. M. Celebi, H. Kingravi, B. Uddin, H. Iyatomi, Y. Aslandogan, W. Stoecker, and R. Moss, A methodological approach to the classification of dermoscopy images, Computerized Medical Imaging and Graphics, vol. 31, pp. 362–373, 2007
9. W. Stolz, A. Riemann, and A. Cognetta, ABCD rule of dermatoscopy: A new practical method for early recognition of malignant melanoma, European Journal of Dermatology, vol. 4, pp. 521–527, 1994
10. V. Ng, B. Fung, and T. Lee Determining the asymmetry of skin lesion with fuzzy borders Computers in Biology and Med., vol. 35, pp. 103– 120, 2005