Detection Techniques for Melanoma Diagnosis: A

Performance Evaluation

Deepika Singh

Department of Computer Science

Banasthali University, Jaipur, India

dsdeepikasingh2@ gmail.com

Diwakar Gautam, Mushtaq Ahmed Department of Computer Science and Engineering

Malaviya National Institute of Technology, Jaipur, India gdiwakarI9874481@gmail.com, mahmed.cse@mnit.ac.in

Abstract-Melanoma which is most commonly spread skin

cancer in United States, UK and Australia and it has been estimated that about 12,650 people died from this cancer in the year 2013 in UK. Thus, early detection and diagnosis of the tumor is most important. And according to survey it was found that 60% of claim lodged against Medical Protection Society for incorrect diagnosis by doctors or negligence of medical reports due to

system failure. So, accurate diagnosis is required for effective treatment of melanoma. This paper presents various diagnostic techniques such as Menzies scale method, Seven Point checklist, Asymmetry, Border, Color, Diameter (ABCD)rule based method and Pattern Analysis method for early diagnosis of cancer. The

methodology which is been followed in diagnosis of skin cancer is also presented and a comparative study of various edge detection, an image preprocessing and segmentation technique are proposed. In our study, analysis has been done on number of samples melanoma clinical images and it has been observed that Canny edge detection and preprocessing using Otsu method is the best approach. Due to importance of image segmentation a number of algorithm have been proposed and based on image that is inputted in the algorithm should be chosen to give the best results.

Index Terms-Menzies Scale, Seven Point Checklist, ABCD, Canny Detector, Otsu Segmentation, Preprocessing.

I. IN T RODUC T ION

Carcinoma is a type of cancer that begins in the skin that covers internal organ in the body. Cancer is a disease in which abnormal cells divide without control and spread to other parts of the body through the blood and lymph system. When the cells of body start to grow out of control at the particular site, they may become cancerous. There are many different types of cancer; some of them are leukemia, sarcoma, melanoma and many more. Melanoma is a skin cancer that begins in melanocytes. Melanocytes produce the dark pigment called melanin which is responsible for color of the skin. It can originate in any part of the body which contains melanocytes. Melanoma is mostly common in women and develops mostly on the back of the body. Melanoma is the most commonly spread cancer in United States, UK and Australia. It has been estimated that more than 76,690 new cases of the melanoma are diagnosed in the year 2013.According to American Cancer Society, 12,650 people died from skin cancer in 2013[1]. Thus, if early detection of melanoma is done then it will save the patient life and can be recovered completely[2].

978-1-4799-3140-8/14/$31 .00 ©2014 IEEE

Sometimes it become difficult even for experienced dermatol­ogist to recognize features of malignant pigmented lesion[3]. Thus, interesting developments in computer-aided systems for clinical diagnosis of melanoma are taken place. The aim is to detect malignant lesion from the clinical images and this is performed in three different stages, viz.; identification of unhealthy lesion, calculation and computation of features and classification using various techniques.

Section II discusses the various clinical diagnostic ap­proaches for Melanoma diagnosis. Section III deals with basic performance measures for the diagnostic processes. The different methodologies for the feature extraction of a given melanoma sample is discussed and compared in Section IV. Section V deals with the experimental setup and retrieved results while Section VI concludes the article.

II. CLINICAL DIAGNOS TIC APPROACHES FOR MELANOMA

DIAGNOSIS

Dermatologist uses slides as image storage and each image has one or more lesion which is located on normal skin with distinct colors. Lesion is varied in shape, size, color and saturation. Fig.(l) shows four different types of lesion.

(a) (b)

(e) (d)

Figure 1: Suspected Melanoma Images [10]

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There are several approaches for diagnosis of cutaneous lesion discussed in detail as below:-

A. Menzies Method for diagnosis of melanoma

To diagnose a lesion to be malignant or non-malignant it must have neither of both negative features and one or more of nine positive features.

Table 1: Menzies scale feature table [41 Negative Features (cannot be Positive Features (atleast one must present) be present)

• Symmetry of lesion • Blue·White veil • Presence of single color • Multiple Brown dots

• Pseudopods • Radial Streaming • Scar·like depigmentation • Black dots/globules • Multiple 5·6 colors • Multiple blue· gray dots • Broadened network

• Symmetry of Pattern: It is required across all axes through the center of the lesion.

• Single color: Black, Gray, Blue, Dark Brown, Tan and Red are scored. White is not scored as a Broadened network color.

• Blue-White veil: An irregular and unstructured area of confluent blue pigmentation with an overlying white "ground-glass" haze.

• Multiple Brown dots: An area of multiple dark brown dots

• Pseudopods: Bulbous and kinked projections, found at the edge of lesion which is directly connected either to tumor body or pigmented network.

• Radial Streaming: Finger like extension at the edge of a lesion that are not distributed regularly or symmetrically around the lesion.

• Scar-like depigmentation: Areas of white, irregular exten­sion.

• Peripheral black dots/globules: Black dots or globules found at or near the lesion.

• Multiple colors: These are Black, Gray, Blue, Dark Brown, Tan and Red.

• Multiple blue or gray dots: Areas of multiple pepper like blue or gray dots.

• Broadened network : Areas of thicker cords of the net.

B. Seven-point Scale

The seven point check list is the diagnostic method which requires identification of seven dermoscopic criteria of the image. A scale is define from 1 to 7, which uses major and minor criteria to grade the lesion. Presence of major criteria adds two points and presence of minor criteria adds one point. If the score of the lesion is at least three points then that lesion is malignant. Table 2 contains scoring criteria.

T bl 2 S a e : even p . omt sca e criteria an d scorme ta bl [4] e I Criteria Points

Major 2 A typical net pigmentation 2 A typical Pattern 2 Blue-white veil Minor I Irregular streaks l Irregular pigmentation l Irregular spots/globules l Areas of Regression Score <-3 non melanoma

>=3 suspected melanoma

C. TDS Score Based on ABeD rule

ABCD feature is the important information based on morphology analysis of the lesion and calculation of Total Dermatoscopic Value (TDV). ABCD feature is Asymmetry, Border Irregularity, Color variation and Diameter features described as follows:e

1) Asymmetry: The image is divided into two perpendicu­lar axes that are positioned in such a way so that they produce a lowest possible asymmetry score. If the image shows asymmetry properties with respect to axes , the asymmetry score is 2. If image shows asymmetry on one axes then the score is 1. The score will be 0, if asymmetry is absent.

2) Border: The image of the lesion is divided into eighths and a sharp, abrupt cut-off of the pigment pattern at the periphery within one eighth has a score 1. Image with score 0 has a gradual, indistinct cut-off.

3) Color: Cancerous skin is characterized by three or more colors. Black, Blue-Gray, Dark Brown, Light-Brown, Red and White are counted in the color score. About five six colors are present in malignant melanoma.

4) Diameter: A malignant lesion will have diameter more than 6mm. Once all the features of ABCD are evaluated for the image, the calculation of TDV score is done[7]. TDS is a uniform system used for dermatoscopy assess­ment and is defined by:

TDS = A * 1.3 + B * 0.1 + C * 0.5 + D * 0.5[5]

It is used to access the lesion and gives information about the lesion whether is mild, suspicious or malicious. A high ABCD score means a lesion is more likely to be malignant melanoma (TDS > 5.45)[6]

D. Pattern Analysis method

Pattern Analysis method seek to identify specific patterns which may be global or local. Global patterns can be Reticular, Globular, Cobblestone, Homogenous, Starburst, parallel, Mul­ticomponent, Nonspecific. Local patterns are Pigment network, Streaks, Globules, Hypopigmentation, Blue-Whitish veil, Re­gression structures ,Vascular structures, Regression structures.

III. DEFINITIONS AND DISCUSSIONS OF MEASURES OF PERFORMANCE IN THE

DIAGNOSIS PROCESS

Dermatoscopy is the primary and most commonly used method of diagnosis of skin cancer. This method is non-

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invasive and requires good experiences to make correct diag­nosis of the cancer. As described by Menzies et aI., in the diagnosis of skin lesion only experts have 90% sensitivity and 59% specificity, as shown in Table 4. Sensitivity and Specificity are calculated from following equations

Sensitivity= (TP)/( (TP)+(FN)) Specificity=(TN)/((TN)+(FP)) where, TP: True Positive , TN: True Negative FP: False Positive, FN: False Negative These parameters are described in Table 3:

Actually Normal

Diagnosed as Abnormal

False Positive(FP) True Negative(TN)

Diagnosed as Normal

T bl 4 S a e : ensltlvlty an d S 'fi' [4] speci city

I I Sensitivity Specificity

Experts 90% 59% Dermatologists 81% 60% Trainees 85% 36% General Practitioners 82% 63%

Based on available information obtained in the clinical examination, the diagnostician performs classification task and take the decision of patient health, whether it is good or not.

IV. METHODOLOGY

There are various stages in the process of diagnosis of melanoma skin cancer. These are image acquisition, prepro­cessing, feature extraction, segmentation and feature score cal­culation using various diagnostic techniques and classification of various image patterns.

A. Image acquisition

The first step involves the acquisition of the digital image of the tissue. The most important technique used for this purpose are the epiluminence microscopy (ELM or dermoscopy) and image acquisition using still or video cameras. ELM provides more detailed inspection of the surface of pigmented lesion by making features more clear, translucent and visible. New techniques have been presented which uses multispectral im­ages[8], direct acquisition of images using charged couple device (CCD)[9].

B. Preprocessing

Before analysis of any image preprocessing should be performed so that all the images will be consistent in their desired characteristics. In the image preprocessing desired features of the image are extracted and examined. Steps which are performed in preprocessing of image are:

1) Input image (Fig.(2.a)). 2) Removing hairs from the image by median filtering. 3) Convert image from RGB format to Gray scale format. 4) Noise removal from the Gray scale image. 5) De-blurring gray scale image.

6) Increasing contrast of the image. 7) Conversion of image into binary image using threshold. 8) Edge Detection using Canny method.

(a) RGB Image (b) Hair Removed Image

(c) Noise Removal (d) Binary Image (e) Canny Edge Detection

Figure 2: Preprocessing Steps

I) Preprocessing using Median Filtering: The median fil­tering is the non-linear digital filtering technique which is used to remove noise. It is widely used technique in image preprocessing as it preserves edges while removing noise as depicted by Fig.(2.b).

2) Image Segmentation: Image processing operations aims at better recognition of objects, i.e. finding suitable features that can be distinguish from other objects and from the background[15].Segmentation subdivides an image into its constituent regions or object[16].There are various segmenta­tion techniques, some of these are proposed in this paper.Image thresholding is one of the important application in image segmentation due to its intuitive properties and simplicity in implementation. Fig.(3) represents two types of thresholding global thresholding and local thresholding for the input image shown in Fig.(4.a)[16].

(a) Global(threshold=0.50) (b) Local(threshold=0.4980)

Figure 3: Types of thresholding

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Preprocessing of image using Otsu's method: Otsu method automatically performs clustering based image thresholding. In this algorithm, it is assumed that the image to be thresholded contains two classes of pixel or bimodal histogram and then calculation of optimum threshold separating two classes is done so that their combined spread is minimal. In this method we search for threshold which minimizes the interclass vari­ance, defined as weighted sum of variances of two classes:[12]

Algorithm 1 Otsu Segmentation Algorithm I) Compute probabilities and histogram of each intensity

level 2) Set up initial Wi(O) and lti(O)

3) Step through all possible thresholds t = 1 upto maxi­mum possible intensity

a) Update wiand Iti b) Compute cr�(t)

4) Desired threshold corresponds to the maximum cr�(t) 5) Gray level with maximum cr� (t) represents threshold

value.

Weights Wi are the probabilities of the two classes separated by a threshold t and cr;variance of these classes.

(a) Input Image (b) Otsu Segmentation Re­sult

Figure 4: Otsu Segmentation Method

Otsu shows that minimizing the inter-class variance is the same as maximizing inter-class variance:

which is expressed in terms of class means Iti and class probabilities Wi

The class probability is computed WI (t) is computed from the histogram t:

WI(t) = �bP(i)

while the class mean Itl (t) is:

Itl (t) = [��p( i)x( i)/WIJ

where x( i) is the value at the center of ithhistogram bin.

Similarly, we can compute W2(t) and Itt(t) on the right side of the histogram for bins which is greater than t.[14]. The Otsu segmentation result for input image Fig.(4.a) is represented by Fig.(4.b).

Region Based Segmentation: Region based segmentation method partitions an image into regions according to char­acteristics such as grayness, texture or color and so on. Let R be the image. Segmentation is the process the partition R into n subregions RI,R2, ....... Rn, , such that:[IS]

a. U�=l Ri = R b. Ri is a connected region, where i=I,2,3 .. n c. R n Rj = ¢for all i and j, i f= j d. P(Ri) = TRUE for all i = 1,2, .. n

e. P(R n Rj) = FALSE for all any adjacent regions P(Ri) is the logical predicate defined over the points in the

set Riand ¢ is the null set. Region growing is a procedure that group pixels or sub

regions into larger region based on predefined criteria for growth. The approach is to start with "seed" point Fig.(5.b) and from this the region grow by appending to each seed those neighboring pixels that have predefined property similar to seed as shown in Fig.(5.c).

(a) Input Image

(b) Initial Seed Image (e) Result of Region Growing

Figure 5: Region Growing Based Image Segmentation

Region Splitting and Merging : It is an alternative to the Region growing method[16]. In this an image is subdivided into disjoints regions and then to merge or split the region in an attempt to satisfy the following first three conditions of region growing method. Following are the steps of Region Splitting and Merging[IS]:

I. Split any region Ri into four square regions where P(Ri) = FALSE

2. Merge any adjacent region Rj and Rk for which P(Rj URk) = TRUE

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3. Stop when no further merging and splitting is possible. Otherwise repeat step 1 and 2.

(a) Segmented Image (b) Original outlined image after Split and Merge

Figure 6: Split and Merge Segmentation

In this technique, it is difficult to identify the point to split a region and thus it does not provide a unique solution. For the input image Fig.(5.a), the results for split and merge based segmentation are shown in Fig.(6.a) and Fig.(6.b).

Segmentation using Watershed Transform: Separating touching objects in an image is one of the more difficult image processing operations. The watershed transform is of­ten applied to this problem. The watershed transform finds "catchment basins" and "watershed ridge lines" in a global thresholded image by treating it as a surface where light pixels are high and dark pixels are low. A tool used in conjunction with watershed segmentation is distance transform. It can be computed using function bwdist as shown in Fig.(7.b), syntax:

D = bwdist(f);

(a) Negative Image (b) Segmented Image using Watershed Trans­form

Figure 7: Watershed Image Segmentation

The gradient method is often used for preprocessing of grayscale image using watershed transformation for segmen­tation. The gradient magnitude image contains higher pixel values along object edges and low pixel value elsewhere, shown in Fig.(S.a). Direct application of watershed transform to a gradient image may sometimes leads to over segmentation due to noise and other local irregularities of the gradient. A approach is used to prevent over segmentation which is based on markers. A marker is the connected component belonging to an image. These markers modify the gradient in the image. Fig.(S.b) represents marker controlled segmentation.

r ''''' ..... �...",.

(a) Watershed Gradient(b) Marker Controlled Im-Image age

Figure S: Watershed Gradient and Marker Controlled Image

Among the entire Watershed segmentation algorithms wa­tershed marker controlled segmentation yields the best results and from all these preprocessing technique we analyzed that preprocessing using Otsu method gives the optimum result.

3) Comparison of Various edge detection algorithms:- :

Image edge detection significantly reduces the amount of data and filters out unwanted information, while keeping and preserving the important structural properties in an image. There are various ways to perform edge detection and these are grouped into two broad categories that is Gradient based edge detection and Laplacian based edge detection[lO]. The Gradient based edge detection detects the edges by looking for maximum and minimum in the first order derivative of the image. Sobel, Prewitt, Roberts are the Gradient edge detection techniques[16]. The Laplacian method searches for zero crossings in the second derivative, to find edges of the image as shown in Fig.(lO) for the input image Fig.(9). An edge of an image has the one-dimensional shape of a ramp and calculating the derivative of the image can highlight its location[ll].

Figure 9: Image used for Edge Detection Analysis

Figure 10: Laplacian of Guassian Edge Detection

Edge detection of four other different types of algorithm i.e. Prewitt, Roberts, Canny, Sobel and LoG filter was performed and shown in Fig.(ll).

2014 International Conference on Signal Propagation and Computer Technology (ICSPCT) 571

l'IIoea_,-..na_

� ga It -. ". I f.:"

Figure 11: Prewitt, Sobel, Canny, Roberts Edge Detection

Among all the edge detection algorithm Canny yields the best edge detection algorithm as it has better detection in noise conditions and thus improved signal to noise ratio.

Line detection using Hough Transform: For a given image, Hough Transform is utilized to find the subset from a set of points, that lie on given parametric line equation[16]. The sparse( varargin) function in Matlab is used which make sparse matrices, which is the matrices containing small number of nonzero elements. Fig.(l2) represents edge detection by Hough Transform.

(a) Hough Transform (b) Line Detection using Hough Trans­form

Figure 12: Hough Transform based edge detection

V. RESULTS

The clinical melanoma samples taken for the experimental purpose is depicted by Fig.(1.a-1.d) respectively. The median filtering removes the hairy features of Fig.(2.a) and is shown in Fig.(2.b). Fig.(2.(d),(e)) represents the Otsu segmented binary image and Canny edge detection results respectively. Fig.(3-8) depicts the different approaches for image segmentation utilizing global thresholding, Otsu method, Region growing, Split and Merging and Watershed segmentation respectively. The edge detection algorithms using Prewitt, Sobel, Canny, Roberts based kernel detectors is depicted by Fig.(11). Fig(10) and Fig(l2) represents the Laplacian and Hough transform based edge detection.

VI. CONCLUSION

In this article we reviewed various skin cancer diagnostic method among which ABCD rule based is most commonly used method and thus yields good results in the early detection of melanoma diagnosis. We have analyzed that Canny edge

detection is the best approach for edge detection as it improves signal to noise ratio. This technique when used with Otsu method gives the most promising result. So. at last this article comes to a conclusion that, for a given melanoma epilumi­nency image the model comprising of median filtering, Otsu segmentation and Canny edge detection is the best optimistic approach for melanoma diagnosis and detection

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