Computer aided diagnosis

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Chapter 2


2.1 Overview

The association between Computer Aided Diagnosis (CAD) and medical informatics is more than two decades years old, the first attempts started 30 years ago, these attempts used computers for analyzing mammographs and chest radiographs automatically. Using mammography, dermatoscopy and endoscopy as application examples, this chapter will give an overview over currently existing challenges and developments within CAD systems. CAD systems can be helpful to the physician, for example, searching in huge data resulted from computer tomography CT for locating any pathological changes in patients, and also CAD system may track sharp changes in x-ray images, such as in mammography, or may determine suspicious regions in complex images like x-ray images of the thorax.

All Computer Aided Analysis and Diagnosis (CAAD) systems work upon receiving input image from the users, process and analyze the input image, the processing of the input image usually produces a set of important features. These features will be analyzed to generate the required output or what so called diagnosis.

Computer aided diagnosis algorithms must be trained first, secondly validated and then delivered to customers for regular usage. On the other hand, computer-aided diagnosis algorithms are used in corresponding systems all over the world.

This chapter presents definitions and overviews of medical computer aided analysis and diagnosis technology in general, and the Computer Aided Analysis and Diagnosis (CAAD) systems for skin lesions and skin burn in specific. It is important to underline the skin burn injuries in this chapter in order to discover how those injuries evolves, and differentiate between the different skin ulcers. In addition, this chapter discusses the various types of Computer Aided Analysis and Diagnosis (CAAD) systems. Finally, this chapter provides examples of current available Computer Aided Analysis and Diagnosis (CAAD) systems in the world.

2.2 CAAD Systems

Computer Aided Analysis and Diagnosis (CAAD) integrates image information from different medical imaging modalities into new image, which is dynamically performed for serial analysis of the images for a patient, these systems are helpful for making the correct decision for patients during clinical diagnosis and treatment.

The CAAD systems may include the data acquisition, the noise reduction, as well as the basic image processing and analysis, such as the image segmentation, the image registration, the image fusion, the pattern recognition and the image display.

Computer Aided Diagnosis is a series of steps used in medicine field to assist the physician's explanations and findings. They can get a great deal of information when using Imaging techniques in X-ray diagnostics, the radiologist must scan and assess extensively in a short period of time. CAD systems can be used for example with computed tomography CT digital images to search for typical appearances and to determine noticeable sections (possible diseases). CAD is considered quite new interdisciplinary technology that combines fundamentals of artificial intelligence and digital image processing with medical image processing. CAD has been used in the tumors detection. CAD supports the preventive medical checkup in mammography, the detection of lung and colon cancer, As a matter of fact; CAD cannot and may not be substituted with the physician, but it can be as a support. In any case the final diagnosis is the doctor's responsibility.

2.3 Image Processing Technology and CAAD Systems

Image processing of medical images has two purposes. Firstly, the reliability of the physician's diagnosis is greatly enhanced and secondly it is effective, efficient and cost competitive. Each missed diagnosis is a lost opportunity for the patient to recover more quickly. An inefficient image processing method increases the cost of the health care system which is already burdened by high costs. Often, these two competing demands result in tradeoffs that are usually expressed covertly, rather than openly. It is the combination of engineers, radiographers and physicians that can reach the best compromise.

2.4 CAAD systems for Skin wounds and lesions

There are two main challenges in CAD systems have to process, the detection of lesions with in an image and the diagnosis of such detected lesions. For physicians, to decide what the cause of wound is. They usually use direct visual observation regarding appearance of that wound, that appearance may contain valuable information about its cause, severity, timewise change of status, and healing diagnosis. This information is readily extracted using digital imaging. The core purpose of such CAAD systems is to aid physicians in analysis procedure, which contains the lesion boundary detection, the quantification of diagnostic features, the classification into different types of lesions, the visualization, the storage, the database management, etc.

Stolz et al. (1994) defined a diagnosis idea for dermatoscopic images which is the ABCD rule of dermatoscopy. The four different letters means; Asymmetry, Border, Color and Differential structures. A weighted combination of the mentioned criteria provides a total dermatoscopic score that is used for lesions classification. As a matter of fact, those mentioned criteria had adopted in many CAAD researches related to melanoma.