A Novel Method for Detection of ArchitecturalDistortion in Mammogram by ides editor

ACEEE Int. J. on Information Technology, Vol. 02, No. 02, April 2012

A Novel Method for Detection of Architectural Distortion in Mammogram Amit Kamra1, Sukhwinder Singh2 and V K Jain3 1

Guru Nanak Dev Engineering College, Department of IT India, Ludhiana, India. 1 amit_malout@yahoo.com 2 UIET, Panjab University Department of CSE, Chandigarh, India 2 sukhdalip@pu.ac.in 3 SLIET,Deemed to be University,Sangrur, India vkjain27@yahoo.com

Abstract--Among various breast abnormalities architectural distortion is the most difficult type of tumor to detect. When area of interest is medical image data, the major concern is to develop methodologies which are faster in computation and relatively noise free in processing. This paper is an extension of our own work where we propose a hybrid methodology that combines a Gabor filtration with directional filters over the directional spectrum for digitized mammogram processing. The most commendable thing in comparison to other approaches is that complexity has been lowered as well as the computation time has also been reduced to a large extent. On the MIAS database we achieved a sensitivity of 89 %. Index Terms--M ammogram images, automated system, architectural distortion estimation, Directional filter, Gabor filter.

I. INTRODUCTION

stellate distortion. Karssemeijer and Brake [17] proposed a method that is based on statistical analysis of a map of pixel orientation. If increase of pixels pointing to a region is found then pixels are marked suspicious. The authors modified the rejection procedure by sonka by suggesting that the gradient of mammographic image is obtained using the first derivative of the Gaussian with a standard deviation of 1 mm. But special care must be taken to choose appropriate values of standard deviation parameters in order to make the method good enough to consider the strong gradient. Matsubara et al. [18] used notion of morphology to find architectural distortion around the skin. Line structure and concentration index were calculated to identify suspicious regions. Sampat et al. [19] proposed a technique for the enhancement of spiculations, in which a linear filter is applied to the Radon transform of the image. The enhanced image is filtered with radial spiculation filters to detect spiculated masses and architectural distortion. Though these approaches were proposed for the estimation of architectural distortion, the estimation complexity for such approach is observed higher. Each of the method consists of lot of mathematical calculations, analytical reasoning and lot of expertise. There was a need to develop a low complex algorithm for the estimation of architectural distortion. In this paper a hybrid method of Gabor filtration with the incorporation of directional filter is proposed for the detection and extraction of orientation fields in the mammogram samples.

Breast cancer is the leading and second most mortal disease in women. The growth of breast cancer in India is on the rise and is rapidly becoming the number one cancer in females pushing the cervical cancer to the second spot [1]. Diagnosis of huge amount of images is a very time consuming process which needs a lot of expertise. So there is a need for Computer-aided diagnosis for screening mammogram which may help the radiologists in interpreting mammograms [2]. Literature survey reveals that different algorithms, for CAD system have been suggested and developed using wavelets [3],[4],[5],[6] statistical analysis [7],[8] fuzzy set theory [9],[10],[11] etc. for the automation of mammogram analysis. But fewer than half of the cases of architectural distortion II. GABOR FILTERING & ORIENTATION FIELD DETECTION have been detected by the existing methods as well as by the Since Breast contains several piecewise linear structure available CAD systems. such as ligaments, ducts and blood vessels that cause Architectural distortion is a situation where the normal oriented texture in mammograms. The presence of architecture of the breast is distorted with no definite mass architectural distortion is expected to change the normal visible [12]. This includes spiculations radiating from a point oriented texture of the breast. Gabor filters are used mostly in and focal retraction or distortion at the edge of the determining the oriented texture in image processing. Gabor parenchyma. It is best perceived at the interface between filters have been presented in several works on image breast parenchyma and subcutaneous fat [14]. Ayres and processing [20], [21], [22], however, most of these works are Rangayyan [13-16] used the concept of Gabor filters and phase related to segmentation and analysis of texture. A 2-D Gabor portrait maps to characterize oriented texture patterns in function can be specified by the frequency of the sinusoid mammograms to detect architectural distortion.The method and the standard deviations and of the Gaussian envelope. worked well to detect peaks related to architectural distortion. [23]. However the procedure does not give good results in case of 11 ÂŠ 2012 ACEEE DOI: 01.IJIT.02.02. 82

ACEEE Int. J. on Information Technology, Vol. 02, No. 02, April 2012 architectural variation from the obtained output. Buciu and Gascadi [24] have described an approach where mammogram sample is filtered using Gabor wavelets and directional features are extracted at different orientation and frequencies. Directional filter estimation (DFE) for image processing to estimate directional flow was introduced by Bamberger and Smith [25].The filters has overcome the limited directional selectivity of separable wavelets. It has been used in various imaging applications such as texture classification [26], image denoising [27], fingerprint image enhancement and recognition [28].For the extracted average Gabor output the directional filter is applied to extract the 2D- directional variations on which the region of interest is extracted. The filtered input image usually has smooth regions with texture regions variation at distorted and non-distorted region. In order to achieve the directional variations, eight channel filter bank, with a tree-structure is proposed [29], which composes of three stages. At each stage, the image is filtered and decimated by a two channel filter bank. The downsampling matrices employed in the tree-structure are Q1, D0, and D1. At the end of stage 3, eight sub bands with (approximately) equal numbers of samples are obtained from the decomposition. For a set of obtained filter output, S = {1, 2, 3, 4, 5, 6, 7, 8} defining the directional subband with modulation index, M = 8 indicating number of directional bands. The design problem can be formulated as an optimization problem with an objective function [30]

Gabor filters are directly related to Gabor wavelets, since they can be designed for a number of dilations and rotations. However, in general, expansion is not applied for Gabor wavelets, since this requires computation of bi-orthogonal wavelets, which may be very time-consuming. Therefore, usually, a filter bank consisting of Gabor filters with various scales and rotations is created.Let be the mother Gabor wavelet, then this self-similar filter dictionary can be obtained by appropriate dilations and rotations of through the generating function.

Where (x0,y0) center of the filter in the spatial domain; total number of orientations desired; and scale and orientation, respectively. The scale factor in (2) is meant to ensure that the energy is independent. Gabor wavelets in the frequency domain is defined as

The design strategy used is to project the filters so as to ensure that the half-peak magnitude supports of the filter responses in the frequency spectrum for the non-effected region as compared to the region with architectural distortion. By doing this, it can ensured that the filters will capture the maximum information with minimum redundancy. The obtained output for the gabor filter at different orientation reveals the orientation in one particular direction, an average gabor output is derived given by,

(6)

(4) Where I(x,y) is the gabor output obtained at each orientation [27]. For the obtained average image for the Gabor output, the orientation field is estimated by [24]

Here for each direcion the i/p is multiplied with the corresponding filter coefficent ‘h’ and added to get the total cost function after processing in all orientaion. The filter structure for the directional filter is defined by [30].

The average estimated orientation field is then used for the designing of a directional filter for the estimation of the region which is directionally different from the original region, as outlined in following section.

Where k is the iteration for filtration and θ is the obtained orientation field for a given sample. The overall implementation could be summarized as shown in Fig 1. For a given sample, we apply the Gabor filter with different orientation angle up to 180 degrees. To the output obtained at each orientation we evaluate the average orientation, this reveals us the dominant orientation for the given sample. To the obtained average output from Gabor bank we find the orientation field given by equation 5.For the obtained orientation field angle we take theta value as a reference and create a directional filter. The obtained filtered output from the Gabor bank is the passed to this directional filter and as a reference, orientation field is extracted, based on this reference the orientation region is predicted.

III. WHY DIRECTIONAL FILTRATION Basically the orientation can be extracted from the Gabor filter but to find the variation in this output, to distinguish between distorted and non-distorted region, directional filters are used. In direction filters, there are banks of filters with different orientation and the obtained orientation image is passed from this bank, this filter filters the data as per the direction coefficient specified. Where the directions are matched for a particular direction that is been filtered out. We can easily find a distinction in such case where we have an © 2012 ACEEE DOI: 01.IJIT.02.02.82

ACEEE Int. J. on Information Technology, Vol. 02, No. 02, April 2012 The region which has got an architectural variation will be differing in its orientation and based on the applied directional filter the region is predicted.

the Gabor based estimation obtained is as illustrated in figure 2(b).

Figure 2. (a) original sample (b) Obtained magnitude image for the Gabor output

The sample is processed for 1800 orientation at a linear step and a mean average of all obtained Gabor result is carried out.

Figure 1. Proposed system architecture

Proposed Algorithm: 1) Read the input mammogram sample. 2) Apply Gabor filtration with 180 orientations at Uniform step. Eq. (3) 3) Compute the average Gabor output. Eq. (4) 4) Compute the orientation field θ. Eq.(5) 5) Define a directional filter h (k)j for the obtained θ. Eq.(6) 6) Apply the directional filter on the extracted spectral map output. 7) Compute the cost function of the developed filter output. Eq. (7) 8) Repeat the iteration to converge the cost function. 9) Obtained direction map at the lowest cost function is one direction region extracted.

Figure 3. Extracted Non-distorted region

For the selected non-distorted region from the obtained magnitude image the spectral density is observed shown in figure 4-8. The observation illustrates that the spectral density varies at average density of 3-4 units for the non-architectural region, where as a spectral density variation with architectural variation is observed to be at 1-2 variation. With this regard the obtained spectral density for a non-architectural region is comparatively 2 times higher than the architectural region (see figure 4,7). Based on this spectral variation a region of architectural distortion region is estimated. The extracted region is then further processed for distortion region extraction based on the directional filter based approach.

IV. RESULTS AND DISCUSSION The mammogram images used in our experiment was taken from the Mammographic image analysis society (MIAS).The database contains 19 image sample showing cases of architectural distortion. For each abnormal case, the coordinates of centre of abnormality are provided with the radius of a circle enclosing the abnormality. The widest abnormality corresponds to a radius of 117 pixels while the abnormality corresponding to smallest radius corresponds to 23 pixels. By knowing the location and proper size of abnormality allows us to manually extract the sub images with proper size representing the tumor affected area. The mammogram samples are digitized to 1024 X 1024 pixels at 8bit/pixel. The various parameters chosen for the Gabor filter were having the following values viz. bandwidth 1, phase shift as 0, theta ranging between 0-pi, orientation of 1800 at – π/2 to π/2 Scale, the number of iteration considered is 12. To discard irrelevant information like breast contour, region of interest of size approximately 150x 150 pixels is extracted using ‘imcrop’ function. The red circle in Fig 2.(a) shows the patch depicting showing abnormality being cropped. The spectrum realization for a mammogram sample using Gabor filter for the distorted and non distorted region is illustrated in figure 4. For a Mini-MIAS image sample ‘mdb115’ shown in figure 2(a), © 2012 ACEEE DOI: 01.IJIT.02.02. 82

Figure 4. Spectral plot for the extracted region

Figure 5. Closed contour region plot for the extracted region

ACEEE Int. J. on Information Technology, Vol. 02, No. 02, April 2012 CONCLUSION Architectural distortion is the most difficult breast abnormality to be read due to its subtle characteristics. Gabor features obtained from the ROI are passed through the directional filters, are used for identifying distorted areas. One paper that was also working on the same applications is Matsurba et al [18]. It is difficult to have a direct and fair comparison as methodology used differs. The developed system illustrates that the proposed approach is lower in complexity for architectural distortion estimation in comparison to other proposed methods. The approach of usage of directional filter estimation based on estimated orientation field reduces the estimation iteration as the orientation field estimation over a defined direction is carried out in such an approach. The overall system sensitivity is higher for the developed system, resulting in higher estimation accuracy.

Figure 6. Extracted architectural distorted region

REFERENCES Figure 7. Spectral plot for the extracted architectural distortion region

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Figure 8. Contour region for the extracted architectural distortion region

The contour regions reveals the volume of distortion we have in given region, in case of architectural distortion region the image will suddenly changes its orientation, so we will not be able to develop a close contour, this can give a density of closed contour higher in non-defective region than in defective region. The computation time retrieved for the “mdb115’ was 8.73 sec. The system was tested on all the 19 images and on an average the computation time was nearly 9.45 seconds. For the evaluation of the proposed approach among all the 19 test samples the region isolated for the architectural distortion region is observed to be effectively detected. The test data are then mixed with 15 more false images and the recognition of the region is carried out. Among all 34 tests the region for the estimated architectural distortion correctly recognized is observed to be 17 samples. About 17/ 19 classifications are manually observed correctly recognized giving the system sensitivity of about 89%.

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