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Medical Image Analysis and New Image Registration Technique Using Mutual Information and Optimization Technique Sumitha Manoj1, S

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Medical Image Analysis and New Image Registration Technique Using Mutual Information and Optimization Technique Sumitha Manoj1, S Original Article Medical Image Analysis and New Image Registration Technique Using Mutual Information and Optimization Technique Sumitha Manoj1, S. Ranjitha2 and H.N. Suresh*3 1Department of ECE, Jain University, Bangalore, Karnataka, India 2Final BE (ECE), Bangalore Institute of Technology, vv puram, Bangalore-04, India 3Department of Electronics and Instrumentation Engg., Bangalore–04, Karnataka, India *Corresponding Email: [email protected] ABSTRACT Registration is a fundamental task in image processing used to match two or more pictures taken, for example, at different times, from different sensors, or from different viewpoints. Accurate assessment of lymph node status is of crucial importance for appropriate treatment planning and determining prognosis in patients with gastric cancer. The aim of this study was to systematically review the current role of imaging in assessing lymph node (LN) status in gastric cancer. The Virtual Navigator is an advanced system allows the real time visualization of ultrasound images with other reference images. Here we are using gastric cancer MRI, gastric cancer CT and gastric cancer PET scanning techniques as a reference image and we are comparing all the three fusion imaging modalities. The objective of this research is to registering and fusing the different brain scanning imaging modalities by keeping Ultrasound (US) as base image and Positron Emission Tomography, Computed Tomography and Magnetic Resonance Imaging as a reference image, the efficiency and accuracy of these three fused imaging modalities are compared after fusion. Fused image will give better image quality, higher resolution, helps in image guided surgery and also increases the clinical diagnostic information. Keywords: Image registration, Virtual navigator, Image fusion, Gastric cancer. INTRODUCTION Gastric cancer is the fourth most modalities with regard to one and the same common cancer and the second leading anatomic area. The intent is to combine cause of cancer-related death world wide .In anatomic and functional image information. 2002, about 934 000 people were diagnosed Specific examples of systems where image with gastric cancer, and approximately 700 registration is a significant component 000 died of the disease1. Image fusion is the include matching a target with a real-time combination of two different imaging image of a scene for target recognition, American Journal of Computer Science and Information Technology www.pubicon.info Suresh et al__________________________________________________ ISSN 2349-3917 monitoring global land usage using satellite resolution picture, and reduces the images, matching stereo images to recover randomness, redundancy in order to increase shape for autonomous navigation, and the clinical diagnosis information. aligning images from different medical modalities for diagnosis The development of Literature survey multimodality methodology based on As imaging technology continues to nuclear medicine (NM),positron emission evolve9, the purpose of this study was to tomography (PET) imaging, magnetic systematically review the current role of resonance imaging (MRI), and optical imaging in assessing LN status in gastric imaging is the single biggest focus in many cancer. This study reviews the role of imaging and cancer centers worldwide and imaging in discriminating node-negative is bringing together researchers and from node-positive patients rather than its engineers from the far ranging fields of role in assessing nodal stage according to molecular pharmacology to nanotechnology the TNM or Japanese Gastric Cancer engineering. This paper presents a new Association (JGCA) classifications.Image technique for registration of multimodal registration is the process of transforming images (CT and MRI) using mutual different sets of data into one coordinate information. system1-4. Data may be multiple The Virtual Navigator is an photographs, data from different sensors, advanced system allows the real time times, depths, or viewpoints. This visualization of ultrasound images with Registration is used in medical imaging, other reference images. Here we are using computer vision, in military, comparing gastric cancer MRI, gastric cancer CT and images, analyzing satellites images. In gastric cancer PET scanning techniques as a image registration two images are involved- reference image and we are comparing all the reference image and test image. the three fusion imaging modalities. The The reference image is denoted by f1 combination has, as the final result, the real (x) and test image is denoted by f2 (x), time data fusion which allows increasing the where x is the coordinates of images. If T is accuracy and confidence of ultrasound a transformation of coordinates then f2 scanning, by overlaying the different images (T(x)) is associated to reference image f1 or visualizing them side by side. (x). We need to find the transformation T The superimposition of US to the such that it gives maximum similarities previously acquired MRI, CT and PET between reference image and test image volume consisted of two procedures with the help of an optimization method. depending on the operator skill and T = Argmax Metric [f1 (x), f2 (T(x))]. experience. One is external marker The organization of the paper is as registration and another is internal marker follows. Section I presents a general outline registration. In external marker registration to the image registration process. A we are using a point based rigid registration. comprehensive literature survey on image The internal marker registration is obtained registration methods applied in the medical by scanning from any available ultrasound image analysis is given in Section II. The window. The common registration used is Virtual Navigator algorithm is presented in External Fiducial Marking acquired with the Section III. Section IV deals with medical two modalities was improved using facial image registration methods. Results anatomical landmarks. By fusing the explained in section v. Finally, the work is images, it will give better performance, high concluded in Section VI. AJCSIT[3][1][2015] 079-088 Suresh et al__________________________________________________ ISSN 2349-3917 Image registration Medical image registration methods Frame work of medical image registration is shown in figure 1, and the Rigid registration components involved are presented in the Rigid image registration models following subsection. (See figure 1.) assume that the transformation that maps the Image registration involves aligning moving image to the fixed image consists two or more images before they can be only of translations and rotations, while meaningful overlaid or compared. Medical deformable models allow localized image registration is applied to three major stretching of images. Rigid models are areas: sufficient in certain circumstances. Point- 1. Multimodality Fusion (overlaying) based rigid registration is commonly used of images that provide complementary for image-guided systems. One set of point information (typically structural and is to be registered to another set of functional). corresponding points by means of a rigid 2. Serial comparison to quantify transformation of the first set. Surgical disease progression or regression especially guidance systems based on preoperative in response to treatment. images, such as CT or MRI; typically 3. Intersubjective registration which employ a tracking system to map points is aimed at creating a normalized atlas from image to the physical space of the representative of a patient population. operating room. For neurosurgery and ear surgery, because of the rigidity of the skull, METHODOLOGY the point mapping is typically a rigid transformation. The transformation is Virtual Navigator is the fusion usually based on fiducial markers that are technology which uses Intraoperative attached to the head before imaging and Ultrasound (US) as base image and remain attached until the procedure begins. Preoperative PET (study analysis purpose)/ A fiducial point set is obtained by localizing CT/MRI as reference images. First we give each fiducial marker both in the image and US and PET, US and CT, US and MRI as in the operating room. Then, a least-squares input images then these brain scanning problem is solved to register the image imaging modalities are registered using points to their corresponding physical point based rigid registration and manual points, and the result is the rigid registration (by selecting control points), the transformation. Fiducial localization error registered images are fused using Rigid (FLE) causes registration error, and the Transformation and Spatial Transformation. least-squares approach is used to obtain the We compare the accuracy and efficiency transformation that minimizes this error in after the fusion of these three imaging fiducial alignment. modalities Image Fusion of Ultrasound with different Imaging Modalities using Virtual Deformable registration Navigator with ultrasound. These fused There are many existing approaches images will give better image quality, higher to deformable registration: Splines-based resolution and it decreases the randomness, transformation models are among the most redundancy and helps in image guided common and important transformation surgery, increases the clinical diagnostic models used in non-rigid registration information. (See figure 2.) problems. Splines-based registration algorithms use control points in the fixed and moving images and
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