Optimized Algorithm For Medical Image Recognition Biology Essay

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The rapid growth and abundance of medicinal imaging technology is revolutionizing medicine. With medicinal imaging playing an increasingly prominent role in the identification and treatment of disease, the medical image analysis society has become preoccupied by the challenging problem of extracting, with the assistance of computers, clinically useful information concerning anatomic structures imaged through CT, MRI, PET, and other modalities. Although modern imaging devices provide excellent views of internal anatomy, the use of computers to analyze the embedded structures with precision and efficiency is limited. Accurate, repeatable, quantitative data must be efficiently extracted in order to support the spectrum of biomedical investigation and clinical activities from diagnosis, to radiotherapy, to surgery. So the main idea of Medical Image Recognition is the extraction of interest regions with high accuracy. This research report includes the study and analysis of existing algorithms for Region of Interest (ROI) extraction of lungs from CT images and implementation of an optimized algorithm. The overall process is divided into five stages and implementation of all these stages is discussed.

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KEY WORDS: Region of Interest, CT images, MIR, Rule based reasoning.

CHAPTER 1

INTRODUCTION

1.1 Digital Image Processing and Computer Vision

The field of digital image processing refers to processing digital images by means of a digital computer. A digital image is composed of a finite number of elements, each of which has a particular location and value. These elements are usually referred to as picture elements pixels. Generally speaking, digital image processing encompasses processes whose inputs and outputs are images and, in addition, encompasses processes that extract attributes from images, up to and including the recognition of individual objects.

The first computers powerful enough to carry out significant image processing tasks appeared in the early 1960s. Initially the work was done in using computer techniques to correct image deformation in the images sent by space probes. In the late 1960s, digital image processing techniques began to be used in medical imaging, remote Earth resources annotations, and astronomy. The innovation of Computerized Tomography (CT) scans in 1970s proved to be an important event in the application of image processing in medical diagnosis. From the 1960s until the present, the field of image processing has grown dynamically. Computer procedures are used to enhance the contrast or code the intensity levels into color for easier analysis of X-rays and other images used in industry, medicine, and the biological sciences. Image improvement and reinstatement procedures are used to process degraded images of unrecoverable objects or experimental results too costly to duplicate. Successful applications of image processing concepts can be found in astronomy, biology nuclear medicine, law enforcements, defense, and industrial applications.

The field of Computer Vision has the ultimate goal of using computers to imitate the human vision, including learning and being able to make inferences and take actions based on visual inputs. This area itself is a branch of artificial intelligence (AI) whose objective is to imitate human intelligence. The field of AI is in its earliest stages of immaturity in terms of development with progress having been much slower than originally anticipated.

1.2 Image Representation

An image may be defined as a two-dimensional function, f(x, y), where x and y are (plane) coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the intensity or gray level of the image at that point. When x, y and the amplitude values off are all finite, discrete quantities, we call the image a digital image. Thus image is available as a two dimensional array for processing

Medical Image Recognition

The rapid growth and increase of medical imaging technologies is revolutionizing medicine. Medicinal imaging allows scientists and physicians to collect potentially life-saving information by peering noninvasively into the human body. With medicinal imaging playing an increasingly important role in the identification and treatment of disease, the medical image analysis community has become preoccupied by the demanding problem of extracting, with the assistance of computers, clinically useful information regarding anatomic structures imaged through CT, MRI, PET, and other modalities. Although modern imaging devices provide excellent views of internal anatomy, the use of computers to enumerate and examine the embedded structures with accuracy and effectiveness is restricted. In order to support the spectrum of biomedical investigation and medical activities from diagnosis, to radiotherapy, to surgery, accurate, repeatable, quantitative data must be efficiently extracted. So the main idea of Medical image recognition is the extraction of interest regions with high accuracy. Medical image recognition meets three main objectives:

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Improve the quality of diagnosis

Increase therapy success by early detection of cancer

Avoid unnecessary biopsies.

1.4 Lung Cancer and CT Images

Lung cancer is known to be the form of cancer with the smallest survival rate after the diagnosis, by a gradual increase in the number of deaths every year. For this reason, the sooner it is detected, the greater the possibility of cure. Lung cancer is due to a special sort of structure known as nodule. The early detection of lung nodules is vital, either for close observation or biopsy to differentiate between benign or malignant nodules, or for timely therapy. The most common methods used to detect pulmonary nodules include chest X-ray and CT.

Fiber-optic bronchoscopy is al so used, but has limited value for finding nodules other than those in the larger airways. Recently MRI is also used but it is not very successful as the patient has to hold his breath for 2-3 minutes. CT offers better contrast than chest X-ray between nodule and background with no overlapping structures, and several studies have shown that CT can detect smaller, earlier stage nodules with a higher sensitivity than chest X- ray. CT technology has undergone a major evolution by the introduction of multi-slice technology. With multi-slice CT, a full-lung, thin-slice (< 1 mm) scan can be done within a single breath-hold. It is hoped that, with the high-resolution CT data available from multi- slice CT scanners, cancerous nodules can be recognized while still small and in an early stage of lung cancer. Many researchers assume that this down-staging effect achieved by early detection of lung cancer will ultimately improve the survival rate [1]. Moreover, it is hoped that lung cancer screening of high-risk patient groups may significantly increase the rate of lung cancer cases which are diagnosed before the cancer has metastasized. Because of this dominance of CT images over X-rays in m using CT scan Images in the thesis.

1.5 Problem Statement

Analysis of existing algorithms for ROI extraction of lungs from CT images and implementation of optimized algorithm.

1.6 Input/Output of the System

The input to the application is the Computed Tomography (CT) images in the bitmap format and the output will be the extracted lungs. A sample input is shown

Fig 1.1 Sample Input Image

1.7 Motivation

With its great success in diagnosis of different diseases CT images are increasing rapidly and radiologists are facing problems in controlling this huge amount of data.

With multi-slice CT images there are hundreds of CT images generated for a single person. The radiologists have to interpret the whole data to get a reading. Humans are limited in their ability to detect and diagnose disease during image interpretation because of their nonsystematic search patterns and the presence of structural noise. The radiologists may miss important patterns because of several reasons like lack of time, fatigue or other human reasons. A complicated anatomy combined with perceptual problems that accompany the projection of a three-dimensional object (the patient) into two dimensions (the image plane), makes identification of lung nodules a burdensome task for radiologists. So the thesis focuses on techniques to address these challenges.