CHAPTER 6 ENHANCEMENT USING HYPERBOLIC TANGENT DIRECTIONAL FILTER BASED CONTOURLET

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1 93 CHAPTER 6 ENHANCEMENT USING HYPERBOLIC TANGENT DIRECTIONAL FILTER BASED CONTOURLET 6.1 INTRODUCTION Mammography is the most common technique for radiologists to detect and diagnose breast cancer. This X-ray image produces an image of the inner breast tissue on film. Mammography is used to visualize normal and abnormal structures within the breasts and therefore, it can help in identifying cysts, calcifications and tumors. It is currently the most efficient screening method to detect early breast cancer. However, due to poor visibility, low contrast and noisy nature of mammograms, it is difficult to interpret the pathological changes of the breast. Therefore, to provide the improved visibility, contrast enhancement of mammogram images becomes important. However, in mammogram, the essential information process closely linked with important edge features. Weak identification of edges is felt as lack of details in human image perception. Therefore, image enhancement process focus to improve the sharpness and explicitness of edges. In mammogram, the intrinsic edge features important for visual information exist at any possible direction and position. In this context, this algorithm focuses on the effective detection of edge information and uses it suitably for image enhancement process. This chapter aims to analyze (i) effective edge determination by hyperbolic tangent (HBT) filter (ii) Use the edge information for enhancement process. This

2 94 chapter has two major parts of descriptions. First part describes the HBT function and its significance in edge information detection on medical images such as CT, Retinal image and mammogram. The second part of this chapter explains mammogram image enhancement based on NSCT structure using 2D HBT directional edge filter as directional filter banks. 6.2 EDGE INFORMATION DETERMINATION BY HBT Let 2D HBT filter is defined by G (x, y) = ( ) for x, y W ( ). (6.1) 0 otherwise In equation (6.1), the region of support for G (x, y) is limited by w to ensure good localization. The filter G is odd symmetric with a single zero crossing at the origin with the slope at the zero crossing point given by /2. HBT filter has a narrower spatial window profile than Gaussian filter for the same and indicates better edge localization with less smoothing effect. Figure 6.1 shows the spatial profile of the second-order differential of filter G(x) for W = 2. Figure 6.1 Normalized 1D Profiles of various functions

3 95 Figure 6.1 compares 1D profile of various functions such as Gaussian, g(x) = exp with zero mean, difference of Gaussian g(x) exp, first order HBT function h (x) = tanh( x) and second order HBT h(x) = (2 tan h(x ) (1 tanh(x ) ). Similar to Gaussian function, the HBT also satisfy criteria like detection, localization and suppression of false edge responses. In order to demonstrate the ability of HBT function in edge feature detection, this work consider steerable simplified HBT to detect edge features and compared with Steerable simplified Gaussian filter Steerable simplified Hyperbolic Tangent Filter (SHBT) Ideally, edge filters have to obtain the responses at any arbitrary position and orientation. Gabor filters are commonly used for extracting local features and simplified Gabor wavelets (SGW) established enhanced performance in edge feature detection than Canny (Wing 2008). Instead of using SGW in ED, this algorithm uses simplified SHBT. In this work, the SHBT filter is defined from quantized hyperbolic tangent as in Figure 6.2 and it is implemented in the steerable method. The steerable characteristics provide multiple orientations, hyperbolic tangent present localization and simplification give less computational cost. The quantized filters are designed by the method described in Wing (2008) and Wei Jiang (2009). The quantization levels and values are obtained by making one of the quantization levels of SHBT is set to zero. As HBT function is anti-symmetrical, the number of quantization levels for positive and negative values are equal. Suppose n denotes the number of uniform quantization levels and the number of quantization levels becomes 2n + 1. The quantization levels for corresponding largest magnitude A,

4 96 q (k) = 2k q (k) = 2k where k = 1,2, n and q (k), q (k) are positive and negative quantization levels. For five quantization levels [q1, q2, q0,-q2, -q1], the simplified HBT filter coefficients are [0.015, 0.03, 0, 0.03, 0.015] in same way. The steerable operated HBT, have following advantages due to their properties (i) multiple orientations by steerable (ii) better tradeoff between feature detection and level of smoothing by HBT and (iii) low running time by quantized HBT. Figure 6.2 1D profile of Hyperbolic Tangent Filter and its five level quantization 6.3 HBT BASE EDGE DETECTION ON MEDICAL IMAGES The HBT based edge determination capable to detect edges in various medical images such as retinal image (fundus), Computed Tomography and mammogram. The effectiveness of the HBT is demonstrated on the corresponding image data sets. They are evaluated both qualitatively

5 97 and quantitatively. The common problem of Gaussian (excess amount of smoothness dislocate the edge information) is illustrated on these images. While operating the DoG filter with smoothing parameter >1, some of the edge information are lost or dislocated. The example illustration of CT images, retinal images and mammogram images are given Figure 6.11, Figure 6.12 and Figure When the smoothing level is = 1 the DoG s edge information in the form of gradients are wider and has poor localization than the SHBT. The SHBT based differentiation provides thin and welllocalized edge information. While Gaussian function s i.e. smoothness increases, the noise influence decreases conversely, edge feature s localization losses. The smoothing provides better noise control, but the edge width increases and hence poor localization of edge features Experimental Results and Performance Comparison The algorithm performance is evaluated on their corresponding image databases. Edge determination performances are evaluated by reconstruction estimation measure as discussed in section through MSE. For twenty CT images, which are obtained from BrighamRAD, the MSE is computed and their values are tabulated in Table 6.1. Figure 6.3 illustrates the results of edge determination of SHBT on CT images. The SHBT based edge determination is compared with Sobel and Canny. In all cases, the MSE is lower in SHBT based method. Lower MSE of SHBT points out that SHBT algorithm has improved performance in edge detection. In the same way, mammogram images and retinal images are tested and presented in the Figure 6.4 and 6.5. The MSE - reconstruction estimation measure for mammogram is tabulated in Table 6.2. Twenty images from MIAS database are considered in this experiment. The testing can be extended for retinal images obtained from DRIVE have the manually drawn ground truth vessel facility. In this case, Figure of merit (F) metric is used to evaluate the detection and localization performance of edge detection. Seventeen images from DRIVE images are considered to compare Canny. From Table 6.3, in all cases, the F improves in HBT based method.

6 98 (a) Figure 6.3 (b) (c) Edge Detection on CT Images (a) Original (b) Difference of Gaussian base Edge detection (c) SHBT base edge detection

7 99 (a) Figure 6.4 (b) (c) Edge Detection on Retinal Images (a) Original (b) Difference of Gaussian base Edge detection (c) SHBT base edge detection

8 100 (a) Figure 6.5 (b) (c) Edge Detection on Mammogram Images (a) Original (b) Difference of Gaussian base Edge detection (c) SHBT base edge detection

9 101 Table 6.1 Performance Comparison of Edge detection MSE reconstruction estimation - CT Image Image Sobel Canny HBT Image Sobel Canny HBT Table 6.2 Performance Comparison of Edge detection MSE reconstruction estimation - MIAS Image Image Sobel Canny HBT Image Sobel Canny HBT mdb mdb mdb mdb mdb mdb mdb mdb mdb mdb mdb mdb mdb mdb mdb mdb mdb mdb

10 102 Table 6.3 Performance Comparison of Edge detection Figure of merit (F) DRIVE image with Adam Hoover ground truth Image Canny HBT Image Canny HBT im Im Im im im Im Im im im Im Im im im Im Im im im Since this algorithm makes use of the simplified version of hyperbolic tangent function, the computation time also get reduced. (i.e.).computing time reduced from seconds to seconds while running the steerable Gaussian edge detection and the HBT filter algorithm for an image size , by Pentium 4 processor 2.4 GHz. From the Figure 6.3 to 6.5 the HBT base edge information are well localized and smoothing effect is under control therefore fine details that are very useful for enhancement process are preserved. 6.4 CONTRAST ENHANCEMENT OF MAMMOGRAMS Contrast enhancement is an essential technical aid in applications where human visual perception remains the primary approach to extract relevant information from images.

11 103 The main contribution of this work is twofold 1) Instead of directional filters in NSCT structure, this algorithm uses non-separable multiscale 2D hyperbolic tangent directional edge filters (HTDE) which are applied with two polarized schemes to efficiently capture the edge information. 2) A new method which combines the above collected edge information to sharp the original mammogram and apply the adaptive histogram equalization (AHE) on sharpened image to improve the contrast. This new approach yield significant improvements in edge information detection. In order to reduce the noise effect due to high pass characteristics of the HTDE, it incorporates scale multiplication. The main advantages of this HBT method are (i) collecting anisotropic edge information and (ii) preserving the local features while enhancing. 6.5 ALGORITHM Figure 6.6 describes the new contrast enhancement process. The combination of NSP and hyperbolic tangent directional edge filter (HTDE) resemble NSCT structures in which it has NSP followed by a DFB. The hyperbolic tangent function based simple non-separable edge patterns for different scales and orientations are designed to apply on the high-frequency band of NSP to detect the edge features.

104 f [u, v] f [u, v] Low Pass Filter High Pass Filter 2D- Directional Edge Filter Combining Directional and Scale Information Nonsubsampled Pyramid (NSP) Directional Filter Bank (DFB) f(u, v)

The HTDE filter responses are combined in a new way to provide sharpened image. The AHE, which follows this sharpening process, further enhances the contrast.

12 104 f [u, v] f [u, v] Low Pass Filter High Pass Filter 2D- Directional Edge Filter Combining Directional and Scale Information Nonsubsampled Pyramid (NSP) Directional Filter Bank (DFB) f(u, v) Adaptive Histogram Equalization Figure 6.6 Block diagram Mammogram Enhancement In essence, the NSP gathers locally correlated points where as HTDE seizes the linear edge structure and directions. The HTDE filter responses are combined in a new way to provide sharpened image. The AHE, which follows this sharpening process, further enhances the contrast. The design of HTDE filter is similar to SGW as discussed in section The SGW uses Gaussian function whereas HTDE uses HBT. The 2D directional filters and their corresponding masks are shown in Figure 6.7. For five quantization levels [q1, q2, q0,-q2, -q1], the simplified HBT filter coefficients are [0.015, 0.03, 0, -0.03, ] in the same way. Figure 6.7 Simplified HBT filters. First row: Filter length of five with four directions. Second row: Filter length of three with four directions

13 105 Since edges are typically present in a mammogram image as localized information, a smaller window size of directional patterns are chosen (i.e.) 3 3 or 5 5. Moreover, 2D non-separable masks effectively capture linear edge structures from the anisotropic edge information. Let an image f [u, v] is decomposed into { f [u, v], c [u, v] } coefficients by the discrete NSCT for level where, f [u, v]= f [u, v] and c [u, v] = f [u, v]. and are coefficients of low and, high pass filter of NSP with scale a = 1,2,3 N. is the HTDE, coefficients, which can be used to obtain the oriented filter responses for the angle with scale a. Also, in this scheme is operated on the image data in, a two polarized schemes which are described as in (Olivier 2010). Let two functions are, and and the edge information from HTDE based,, NSCT are computed by, = (, ) + (, ), where, = f [u, v], and, = f [u, v], ). The enhanced image f(u, v) from, is defined as f(u, v) = f (u, v) +, (u, v) (6.2) f(u, v) is the low pass filtered image, is the parameter used to determine the amount of edge information that is to be processed for enhancement. The sum of all oriented responses gives the total responses and the scale multiplication of edge filter responses provides efficient noise control (Paul Bao 2005). It is easy to interpret the second term in equation (6.2) is a weighted high pass filter and the addition of multiscale anisotropic edge information with original will aid enhancement process. The superimposing of original image and optimum intrinsic line structures and edges from HTDE responses improves the sharpening process. This process enhances the contrast near object

14 106 boundary. The advantage of the method by equation (6.2) not only enhances, but also effectively controls the noise. The multidirectional and multiscale responses that correspond to HF components can be combined by taking either modulus-maxima or maximum of the absolute values or sum of all oriented responses. AHE is to generate an enhanced mammogram image, which has a better visual quality than the original. The stepwise procedure of this method as follows. Step1: Obtain pyramidal decomposition of the input image without down sampling as given in section Step 2: Apply 2D HBT based directional edge filter on the high pass sub bands Step 3: Continue step 1 on low pass sub band for further levels. Step4: Apply developed equation (6.2) to incorporate all features Step 5: Apply histogram equalization for contrast enhancement. 6.6 RESULTS AND PERFORMANCE COMPARISON This new image enhancement has been experimented with all 23 X-ray mammogram images available in micro calcification category of MIAS database. The mammograms are characterized with inherent noise. This method includes two steps (i) gathering the anisotropic edge information from NSCT decomposition and (ii) using the edge information in contrast enhancement process. High pass and low pass filters of NSP filters are applied on mammogram images. This method use any one of the NSP types, (i) spectrally designed filter (Lu 2006) that have better localization than NSCT, without decimation output (ii) filters used in átrous algorithm and (iii) non-sub sampled decomposition filter. All the above ensures the shift invariance a useful property for feature detection. This method chooses the un-decimated version of spectrally designed filter. They not only have the advantage of without aliasing but also, the raised cosine transition helps to

15 107 collect more correlated edge features. The useful correlated edge features are convolved with HTDE masks. The directional masks are simple nonseparable 2D patterns as in Figure 6.7, which are capable to remove the blurred regions in diagonal orientations. The enhanced images are obtained by equation (6.2). The enhancement results are compared with nonlinear unsharp masking (NLUSM) (Karen 2011) and AHE. In order to demonstrate the effectiveness of this enhancement algorithm on mammogram, four of the results are presented in Figure 6.8. From the Figure 6.8, it is recognized that NSCT method provides more efficient sharpened edge structure with less noise effect than other methods. This NSCT technique provides strong contrast enhancement than AHE and NLUSM while preserving local information of the original image. Any contrast enhancement technique is expected to have both strong contrast enhancement and preservation of all local information of the original image, in the sense of optimum contrast enhancement. In this aspect, the new method provides the promising enhancement results. Though the AHE technique provided strong contrast enhancement, it is not so successful to preserve some local information in the input mammogram image and brings out the fine hidden details. Enhancement Measure (EME) introduced in (Agaian 2001) and discrete entropy (H) parameters are used in these performance comparisons. Discrete entropy is used to measure contrast enhancement and it is defined by H = h(r ) log h(r ) h(r ) 0, where ( ) is the normalized image histogram at the gray level. Entropy and EME parameters are tabulated in Table 6.4. The improved EME and entropy measures signify the superior enhancement performance by this NSCT method.

16 108 Figure 6.8 Comparison of various enhancement algorithm: Column 1 original images. Column 2 NSCT Method. Column 3 AHE. Column 4 NLUSM

17 109 Table 6.4 Performance Comparison of various Enhancement Methods Image Original NSCT AHE NLUSM mdb209 EME Entropy mdb211 EME Entropy mdb213 EME Entropy mdb216 EME Entropy mdb218 EME Entropy mdb219 EME Entropy mdb222 EME Entropy mdb223 EME Entropy mdb226 EME Entropy mdb227 EME Entropy mdb231 EME Entropy mdb233 EME Entropy mdb236 EME Entropy mdb238 EME Entropy mdb239 EME Entropy

18 110 Table 6.4 (Continued) Image Original NSCT AHE NLUSM mdb240 EME Entropy mdb241 EME Entropy mdb245 EME Entropy mdb248 EME Entropy mdb249 EME Entropy mdb252 EME Entropy mdb253 EME Entropy mdb256 EME Entropy DETECTION OF MICROCALCIFICATION Enhanced images are utilized as preprocessing stage of a system that aimed at the detection of microcalcification in mammograms. Microcalcifications are deposits of calcium and cluster of this may indicate an early sight of cancer. The enhanced results can be used to detect the smallsized and low-contrast microcalcifications that could be missed or misinterpreted by medical experts. A fixed threshold is applied to various enhanced images and the results are presented in Figure 6.9. The enhanced images along with the original are presented in first row of the Figure 6.9. Microcalcifications of corresponding enhanced image after application of the threshold are given in the second row of Figure 6.9.

19 111 Figure 6.9 (a) (b) (c) (d) Microcalcification Detection (a) original (b) CLAHE (c) NLUSM (d) HTDE based enhancement The results, strongly suggest that this method offers considerably improved microcalcification detection capability over the AHE and NLUSM methods. 6.8 SUMMARY This chapter studies Hyperbolic Tangent (HBT) based edge extraction that is used to enhance the contrast of the mammograms. The edge detection ability of the HBT function proved in medical images such as CT, mammogram and retinal images. Utilization of anisotropic features for the enhancement process is the drawback of the existing methods. The contributions of this developed method are (i) a set of non-separable edge

20 112 filters that are derived from simplified Hyperbolic Tangent function. They are used as directional filer banks in the NSCT structure and linear structures (anisotropic features) captured by this NSCT are used for contrast enhancement process. (ii) The NSCT based geometrical edge information is combined with the original image by a developed method by equation 6.2 followed by adaptive histogram equalization to enhance the contrast of mammogram image. Due to effective usage of geometrical structure and anisotropic features, this developed method has better Enhancement Measure (EME) than recent nonlinear unsharp masking based mammogram enhancement method and common CLAHE. Noise control and fine details enhancement are the advantages of this method. In addition, the detection of microcalcification from this NSCT enhanced mammogram image is better than other methods.

CHAPTER 6 DETECTION OF MASS USING NOVEL SEGMENTATION, GLCM AND NEURAL NETWORKS

130 CHAPTER 6 DETECTION OF MASS USING NOVEL SEGMENTATION, GLCM AND NEURAL NETWORKS A mass is defined as a space-occupying lesion seen in more than one projection and it is described by its shapes and margin