Medical imaging is the process and technique of creating visual representations of interior of a body for medical intervention and clinical analysis. Medical imaging helps to reveal internal structures hidden by the skin and bones as well as to diagnose and treat disease. It also establishes a database of normal anatomy and physiology to make it possible to identify abnormalities.
Goals for MIP
MIP is to meet the following goals:
- To develop computational method and algorithms to analyze biomedical data.
- To develop tools to give our collaborators the ability to analyze biomedical data to support advancement of biomedical knowledge.
Need for MIP
Now a days Imaging has become an essential component in many fields of biomedical research. Biologists study cells and generate 3D confocal microscopy data sets, virologists generate 3D reconstruction of viruses from micrographs, radiologists identify and quantify tumor from CT and MRI scan and neuroscientist detect regional metabolic brain activity from MRI scans. Analysis of these diverse type of image requires sophisticated computerized quantification and visualization tools.
Benefits of Digital Image Processing for Medical Applications
- Interfacing Analog output of sensor such as microscopes, endoscopes, ultrasound etc. to digitizer and in turn to Digital Image Processing system.
- Image enhancements.
- Changing density dynamic range of Black and White images.
- Color correction in color images.
- Manipulating of colors within an image.
- Contour detection.
- Area calculation of the cell of a biomedical image.
- Display of image line profile.
- Restoration of images.
- Smoothing of images.
- Construction of 3-D images from 2-D images.
- Generation of negative images.
- Zooming of image
- Pseudo Coloring.
- Point to point measurement.
- Getting relief effect.
- Removal of artifacts from the image.
Operation performed on Medical Images
Smoothing is the process of simplifying an image while preserving important information. The goal is to reduce noise or unwanted details without introducing too much distortion so as to simplify subsequent analysis.
This is the process of bringing two or more images into spatial correspondence. In the context of medical imaging image registration allow for the concurrent use of images taken with different modalities at different time or with different patient positions. In surgery images are acquired before pre operative, as well as during intraoperative surgery. Because of time constraints the real time intraoperative images have a lower resolution than the preoperative images obtained before surgery. Moreover, deformations which occur naturally during surgery make it difficult to relate the high resolution preoperative image to the lower resolution intraoperative anatomy of the patient. Image registration attempts to help the surgeon relate at the two sets of images.
When identifying at an image a human observer cannot help seeing structure which often may be identified with object. Segmentation is the process for creating a structured visual representation from unstructured one. Image segmentation is the problem of partitioning the image into homogeneous region that are semantically meaningful that correspond to objects. Segmentation is not concerned with actually determining what the partition is. In the context of medical imaging these regions have to be anatomically meaningful. Example of Segmentation is partitioning a MRI image of the brain into white and gray matter. Since it replaces continuous intensities with discrete label segmentation can be seen as an extreme form of smoothing information reduction. Segmentation is useful for visualization it allows quantitative shape analysis and provides an indispensable anatomical framework for virtually any subsequent automatic analysis.
Here we sketched some of the fundamental concepts of medical image processing.