Question

Explain in brief all the below questions

1. Biomedical image classification: classification performance evaluation
2. Principles of image compression
3. 3D biomedical image visualisation and processing
4. Colour biomedical image processing

Answer 1

Biomedical image processing is a very broad field; it covers biomedical signal gathering, image forming, picture processing, and image display to medical diagnosis based on features extracted from images. Biomedical image processing is an interdisciplinary field that spreads its foundations throughout a variety of disciplines, including electronic engineering, computer science, physics, mathematics, physiology, and medicine. Several imaging techniques have been developed, providing many approaches to the study of the body, including X-rays for computed tomography, ultrasounds, magnetic resonance, radioactive pharmaceuticals used in nuclear medicine (for positron emission tomography and single-photon emission computed tomography), elastography, functional near-infrared spectroscopy, endoscopy, photoacoustic imaging, and thermography. Even bioelectric sensors, when using high-density systems (e.g., in electroencephalography or electromyography), can provide maps that can be studied with image processing methods. Biomedical image processing is finding an increasing number of important applications, for example, to study the internal structure or function of an organ and in the diagnosis or treatment of a disease. If associated with classification methods, it can support the development of computer-aided diagnosis (CAD) systems, e.g., for the identification of a diseased tissue or a specific lesion or malformation.

Visualization is the process of exploring, transforming, and view data as images to gain understanding and insight into the data, which requires fast interactive speed and high image quality.

Medical image 3D visualization is one of the fundamental processes in medical diagnostics. Using the acquired 3D images it is possible to find the volume of pathology zone, which is the evidence of a specific disease. As a consequence, two problems emerge: visualization of 3D medical images and pathology zone extraction from acquired images.

Available imaging software in some cases provides the construction of 3D images based upon various medical data obtained by computer tomography, magnetic resonance imaging, scintigraphy, etc. But further processing of these images (image segmentation, pathology zone extraction) can result in loss of information during initial image reconstruction. Furthermore, existing medical imaging software does not provide the automatic extraction of regions of interest.

In medical imaging procedures, clinicians base their diagnoses and treatment decisions on the assessment of image data. In most cases, the final stage of the imaging process is the human interpretation of data using visualization approaches and display devices. In the past few years, the use of color in medical images has increased significantly in support of sophisticated visualization approaches. However, the ad hoc manner for handling color and the lack of standardization and common methodologies used to display medical images are often cited as contributing to suboptimal medical decisions with direct impact on patient treatment and prognosis.Functional images are often read using a pseudocolor presentation. Pseudocolor is defined here as the display of color-coded scalar imaging data with no direct correlation with the actual color of the object being imaged. The technique is typically used as a means of highlighting image features of interest.