Locating MRI discrepancy by orientation invariant method

Abstract

This research investigates the techniques using the image subtraction to find the discrepancy between healthy and illness MRI images. The technique developed in this research moves healthy MRI image to overlap with illness MRI image. Then, healthy MRI image and illness MRI image are aligned to same orientation. After the healthy MRI image overlapped with illness MRI image, illness MRI image is subtracted from healthy MRI image. If there is discrepancy between healthy and illness MRI images, after image subtraction, discrepancy will remain in the subtracted result. From beginning to end the inspection is done by machine automatically and there is no further human effort involved. The technique developed in this research can very accurately find discrepancy of healthy and illness images. This paper explains the method using second moment to find orientations of the MRI images. By the orientations of MRI images, healthy and the illness MRI image can be aligned to same orientation. Detailed process of image rotation is addressed in this paper.

Introduction

In the past twenty years, researchers invested a lot of effort to develop different techniques to identify the difference of images. This past work includes – principal component analysis [4], [25], [26], moment invariant [20], trace transform [19], fusion code of real part and imaginary part [15], Eigenspace [8], Limbus and black purple detection [6], [18], Contour angle histogram [1], R-Theta transform [2], Delaunay tessellation [3], calculation of rotating angle [7], [25], 3D to 2D remainder [9], positive correlation [10], 3D to 2D approach [11], [12], congruent transform [5], [14], [23], area discrimination [13], [14], [16], contour following, U-turn feature [17], hook up the missing stroke [21], baseline estimation [22], sharp U-turn extraction [24].

Here, we propose a different approach, which combines several techniques, to cope with MRI image shifting and rotating problems. Thus, after pictures of the MRI images are taken, the algorithms developed here can automatically locate the discrepancy of MRI images without involving further human effort. Fig. 1 shows different kinds of MRI images. At beginning, MRI image digitizer-machine digitizes MRI image and then MRI image is transferred to the BMP file. In this research, there are two kinds of MRI images. The first kind of image is called healthy MRI image. The second kind of image is called illness MRI image. The healthy and illness images are shown in Fig. 6. The discrepancy of these two kinds of images can be detected by the technique developed in this research. Since the backgrounds are irrelevant to find the orientation and centroid of the image, backgrounds are removed from the MRI images. The result is shown in Fig. 2. Major axis and centroid algorithms are used to find orientations and centroids of these two images. Both algorithms can very precisely locate the centroids and orientations of these MRI images. This research first shifts healthy MRI image to make the centroid of healthy MRI image to overlap with centroid of illness MRI image. Then, healthy MRI image is rotated. Thus, healthy MRI image can be aligned to same orientation as illness MRI image. After this, image subtraction can be applied to these two MRI images. The subtracted result can very precisely show the discrepancy of these two MRI images. Under normal circumstance, when MRI images are taken, even though MRI image are a little shifted or a little rotated, the algorithms developed in this research will still correctly find the discrepancy of healthy and illness MRI images.

This paper consists of five sections. Section 2 discusses how to extract the important feature of MRI images. Section 3 explains how to find centroids and orientations of MRI image. Section 4 performs image subtraction. Section 5 concludes this paper.