Intelligent Mammography Retrieval Engine

Raman Valliappan and Patrick then Swinburne University of Technology (Sarawak campus)

Kuching 93576, Sarawak Malaysia

Abstract- The objective of this paper is to develop a

prototype called Intelligent Mammography Retrieval Engine. It can be implemented as a computer software that able to examine/analyst digital mammogram image and automatically yield its finding and recommendation. The computerized examination is done through comparison of a new digital mammogram image with the existing collection of digital mammogram image in the database. The digital mammogram images in the database that exactly identical to the new digital mammogram image will be sources of yielding finding and recommendation of new digital mammogram image.

I. INTRODUCTION

Artificial intelligence encompasses such diverse activities as game playing, automated reasoning, natural language, automatic programming, machine learning, robotics and vision, software tools, modeling human performance, and expert systems for complex decisions [5]. Complex medical decisions are central in each phase of clinical care [4], and are usually based on decision elements or findings derived from a single patient by the clinical team [7]. The discovery of decision elements particular to a given patient is a major task for the clinical team, and a necessary prelude to initiating medical action for the patient. Medical expert systems have evolved to provide physicians with both structured questions and structured responses within medical domains of specialized knowledge or experience [5]. The structure is embodied in the program on the advice of one or more medical experts, who also suggest the optimal questions to consider, and provide the most accurate conclusions to be drawn from the answers the physician chooses. In software programs, these decision sequences are represented in clauses of the form: “If..., Then...”, with final else having positive value in the closed system of the program [8].

Mammography is a sensitive procedure for detecting

breast cancer, but the positive predictive value is low. There are only 10-34% of women who undergo biopsy for mammographically suspicious impalpable lesions are actually found to have malignancy. Between 0.5% and 2.0% of all mammographic examinations result in biopsy; several

hundreds of thousands of biopsies are performed on benign lesions each year. The women undergoing biopsy for a benign finding are unnecessarily subjected to the discomfort, expense, potential complications, and change in cosmetic

appearance, and anxiety that can accompany breast biopsy. The proposed system may significantly improve this performance through a simplified case-based reasoning approach that uses a large database of cases with known outcomes. In clinical practice, this system can be easily integrated into the mammography’s' work flow through a computerized reporting system.

The main objective of the paper is to detect the early

stages of breast cancer by IMRE architecture. The paper is organized as follows: in Section 2 motivation for the proposed work is explained, existing works of mammography detection is explained in section 3. In Section 4 methodology and over all architecture of IMRE is described. The experimental and radiologist validation is explained in Section 5. Finally, conclusion is summarized in Section 6.

II. MOTIVATION

The motivation of this research is due to the fact that the early detection is curable. Screening mammography is performed on patients with no signs of breast cancer and we believe that these mammograms have no sign of clearly abnormality and will be much the same pattern. The image processing technology is becoming accurate and advance capable of produce a goof result on analyzing two images. Early detection is identified and will reduce the breast cancer case and death.

III. EXISTING WORKS

There has been an enormous amount of research in computer aided screening of digital mammogram images. It is represented in a broad array of published articles which describe the development, testing, and application of screening methods. However most of the research specifically international level as well as few in national level has concentrated of abnormalities detection in the image. These include algorithms to enhance and/or segment images, detect/classify calcifications, or detect/classify masses [1][2][3]. Previously research works have evaluated, with CAD systems or observer’s performance studies, lossy compression in digital mammography. Perlmutter et al.[6] evaluated 57 digital mammograms compressed with the set partitioning in hierarchical trees algorithm. They found no difference between analog and compressed images. Good et

al.[7] assessed the detection of masses and clustered micro clarifications, by mean of an ROC study. Zheng et al.[8] compared the performance of CAD system for the detection of primary signs of breast cancer using original images and image reconstructed after JPEG compression. They obtained that JPEG doesn’t effect the CAD scheme for detecting masses, but detection of cluster of macro clarification is affected with compression. In all these research works, they could not detect the earlier stage of breast cancer accurately.

Other works involve combining these algorithms into

more comprehensive systems, in which to assist the radiologist in examining the images for abnormalities detection [2] [4]. These works concentrate on reducing the workload during the examination of the images for abnormalities detection. It reduces the radiologist burden however the existence of radiologist is necessary. They still have to go through to examine all mammograms. Our work different in the sense of it reduces the radiologist involvement in the examination of the images. Our prototype reduces the radiologist involvement in some of the examination stage especially to those who has very little or very hardly known of abnormalities. They should be more focus on cure plan of patient that positively having abnormalities.

IV. METHODOLOGY

We propose a novel approach to computer-aided diagnosis of breast cancer using mammographic findings. Text based expert system has been developed to provide support for the clinical decision to perform breast biopsy. First the user queries the medical image from medical image record database. There the images are been preprocessed to reduce the noise, then the image is segmented in different slices and the defected part alone is segmented and shown as output , Based on the segmented output , decision made throughout the work. The system is designed to aid in the decision to perform a biopsy in patients who have suspicious mammographic findings. The decision to biopsy can be

viewed as a two-stage process. First, the mammography views the mammogram and determines the presence or absence of image features such as calcifications and masses. Second, the presence and description of these features and the patient's medical history are merged to form a diagnosis. The text based expert system is an aid to the second step and is motivated by the large fraction of biopsies that are benign. A. Architecture of IMRE The overall prototype architecture of Intelligent

Mammography Retrieval Engine (IMRE) is shown in the figure 1. The prototype consists of two major components. They are Digital database of mammogram report (DDMR) and Digital mammogram similarity comparison engine (DMSCE).

The Digital Database of Mammogram Report (DDMR) stores real digital mammogram image and its characteristics (e.g.: patient information, clinical history, procedures, finding, impression, recommendation). It also store histogram and texture detail of mammogram images. The digital mammogram similarity comparison engine (DMSCE) is responsible of measuring the similarity between two digital mammogram images. DMSCE have the capability to measure that two mammogram images are exactly similar at the very high level of accuracy.

The prototype work as follows. The new digital mammogram image is inserted to the system. Once in the system, the DMSCE will retrieve mammograms in the DDMR and measure the similarity each of them to the new inserted digital mammogram image. A series of similar digital mammogram images are collected. Its finding and recommendation then will be retrieved from the DDMR to yield the final finding and recommendation of the new inserted digital mammogram image.

Figure 1 illustrates the architecture of IMRE

B. Database Design

Rational database principles are applied in the development of the DDMR component. The draft of E-R diagram is shown below. It consists of eight tables and its

attributes.

Fig 2 illustrates the database design

C. Database of Mammogram Report The elements considered in DDMR are as follows:

Digital mammogram image: Real digital mammogram images.

Patient information: Patient’s name, age, and the reason for the mammogram (i.e., annual screening mammogram, referred by physician to evaluate new right breast lump).

Clinical history: The patient’s medical and family history of breast cancer or other breast conditions. It may also include relevant medications of the patient.

Procedures: Describe types of mammogram views were taken. Typical views for screening mammograms include the cranio-caudal view (CC) and the medio lateral oblique view (MLO).

Findings and impression: Describe what was found from the mammogram. Size, location, and characteristics of breast abnormalities may be noted. Primary signs of breast cancer may include speculated masses or clustered pleomorphic micro calcifications. Secondary signs of breast cancer may include asymmetrical tissue density, skin thickening or retraction, or focal distortion of tissue. Some radiologists may also include comments about breast density and distribution of the breast tissue. This includes the radiologist’s overall assessment of the findings.

D. Digital Mammogram Similarity Comparison Engine

Image processing technology is heavily used in this stage. In general the proposed idea is follow. Image is decomposed into small same size tiles by dividing it into m x n matrix. (m * n) small tiles is derived. Given two digital mammogram image i and j, the similarity comparison is done by comparing each tiles of the same location, that is, tile i[0,0] will be compared to tile j[0,0], tile i[0,1] will be compared to tile j[0,1] and so on until the last tile. An example in figure 2(a) and 2(b) show that two different digital mammograms images shown as image a and b are divided in to 3 x 2 matrix. In this example of figure 2, tile a[0,0] will be compared to tile b[0,0], tile a[0,1] will be compare to tile b[0,1] and so forth to the last tile which is tile a[2, 1] will be compare to tile b[2, 1]. The histogram of each tile then is computed and will be the parameter for similarity comparison. The histogram of an image represents the distribution of gray level intensities in an image irrespective of location in the image. Figure 2(c) and 2(d) shows the histogram characteristics of tile a[0,0] tile and tile b[0,0] respectively. In this example at the first glance, tile a[0,0] and tile b[0,0] are identical since the histogram characteristics are identical. However we further assure by tile texture comparison. The texture comparison is important for the localization of the bits in the tiles. Both histogram and texture comparison gives accurate pattern and texture similarity measurement of two tiles. Once the similarity measurement is completed, similar mammogram images are retrieved and the retrieved result is

sent for validation through text based expert system to gain accuracy.

Fig 2(a)(b) illustrates the mammogram image. Fig2(c)(d) illustrates the histogram of mammogram image.

V. EXPECTED OUTCOMES

The current proposed system is partially implemented in MATLAB. The Mammogram images are collected from digital mammogram database for initial experimental work. Once the prototype is completed, real image will be gathered from kuching general hospital to conduct real time experiment. First the training set of data is inserted to DDMR and DMSCE and it is ready for the experimental task. Then the finding of the experimental is collected. Radiologist will verify and validate of the finding. Finding will be collected and parameter such as percentage of hit, precision and recall are computed; m and n variables are tested. Based on the test, disease is diagnosed faster.

VI. CONCLUSION AND FUTURE WORKS

The paper clearly explains the methodology of how knowledge is applied to retrieve information from medical image data. The Info structure presented in this paper when successfully implemented would have an immense impact in the area of computer-aided diagnosis system and human anatomy image navigation. In future the methodology proposed can be applied in a variety of domain application.

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[2] Kim, Jong Kook and Hyun Wook Park. “Statistical Textural Features for Detection of Micro calcifications in Digitized Mammograms.” IEE Transactions on Medical Imaging 18.3 (1999): 231-8.

[3] Morrison, Steven and Laurie M. Linnett. “A Model Based Approach to Object Detection in Digital Mammography.” Proceedings 1999 International Conference on Image Processing (1999): 182-6.

[4] Cady, Blake and James S. Michaelson. “The Life-Sparing Potential of Mammographic Screening” Cancer 91:9 (2001): 1699-1703.

[5] Luger GF, Stubblefield WA, Artificial intelligence and the design of expert systems, Redwood City, CA Benjamin/Cummings Publ. Co. 1989.

[6] S.M Permutter et.al, “Image quality in lossy compressed digital mammograms”, Signal Process, Vol 59, pp.683-696, July 1997.

[7] W.F Good et.al, “Detection of masses and clustered micro clarification of data compressed mammograms: An observer performance study” Vol 175, pp. 1573-1576., Dec 2000.

[8] B.Zheng et.al, “Applying computer assisted detection schemes to digitized mammograms after jpeg data compression an assessment”, Acad Radiol., vol 7, p.595-602, Aug 2000.