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Why medical image Processing is important ?

An image is an optical representation of a real object. A medical image is the representation of the internal structure or function of an anatomic region in the form of an array of picture elements called pixels or voxels.


What is image processing?


Image processing is a form of signal processing used, to perform operations on an image, in order to get an enhanced image or to extract some useful information from it. Medical image processing includes the analysis, enhancement, and display of images captured by equipment such as X-Ray, Ultrasound, MRI, CT scanners, nuclear medicine, and optical imaging technologies.



Sagittal MRI image of a head had enhanced using (i) image adjustment, (ii) histogram equalization, and (iii) adaptive histogram equalization

SOURCE:https://www.intechopen.com/media/chapter/67331/media/F7.png




A three-dimensional image of a localized, blood-filled balloon-like sac (aneurysm).

SOURCE:https://d3i71xaburhd42.cloudfront.net/7a03974a4b923bd738377e177bfe7b3bd3c9adc2/4-Figure2-1.png


VARIOUS IMAGE PROCESSING TECHNIQUES


1. DATA ACQUISITION


• The first integral step in image formation is the acquisition of raw imaging data. It contains the original information about captured physical quantities describing internal aspects of the body.


• Different types of imaging modalities utilize different physical principles and thus involve the detection of different physical quantities.


• For example, in digital radiography (DR) or computed tomography (CT), it is the energy of incident photons; in positron emission tomography (PET), it is the photons energy and their detection time; in magnetic resonance imaging (MRI), it is the parameters of a radio-frequency signal emitted by the excited atoms; and in ultrasonography, it is the parameters of the acoustic echoes.


2. IMAGE RECONSTRUCTION


• Image reconstruction is a mathematical process of forming an image using the acquired raw data.


• Typical examples of analytical methods include filtered back projection (FBP), widely used in tomography; Fourier transforms (FT), particularly important in MRI; and delay and sum (DAS) beamforming, a technique which is integral to ultrasonography.


3. IMAGE ENHANCEMENT


• Image enhancement refines a transform representation of an image to improve the interpretability of the acquired information. Its methods can be subdivided into spatial and frequency domain techniques.


• The spatial domain techniques operate directly on image pixels, which is particularly useful for contrast optimization.


• The frequency domain methods use frequency transform and are best suited for smoothening and sharpening the images by applying different kinds of filters.


• Utilization of all these techniques allows for noise and inhomogeneity reduction, contrast optimization, enhancement of edges, elimination of artifacts, and improvement of other relevant properties that are crucial for the subsequent image analysis and its accurate interpretation.




Edge-aware local contrast manipulation of leukemia cell images (a) and (c) original image, (b) Edge threshold, and (d) Reduced contrast




Pixel Region of an X-ray CT scan and the Adjust Contrast Tool

SOURCE:https://d3i71xaburhd42.cloudfront.net/e90b01ea0e3936cff68680b57ffcb20d567502e5/2-Figure1-1.png


4. IMAGE ANALYSIS


• Image analysis is the central process in image computing that uses image segmentation, image registration, and image quantification.


• The image segmentation process partitions the image into meaningful contours of different anatomical structures.


• Image registration ensures correct alignment of multiple images.


• The process of quantification determines properties of the identified structures such as volume, diameter, composition, and other relevant anatomical or physiological information.


• All these processes impact image quality.


5. VISUALIZATION


• The visualization process renders the image data to visually represent anatomical and physiological imaging information in a specific form over defined dimensions.


6. IMAGE MANAGEMENT


• The final part of medical image processing deals with the management of the acquired information and encompasses various techniques for storage, retrieval, and communication of image data.


• For example, the medical imaging technology picture archiving and communication system (PACS) provides economical storage and access to images from multiple modalities and the DICOM standard is used for storing and transmitting medical images



SOURCE:https://images.squarespacecdn.com/content/v1/558bc95ee4b05a9e214fb070/1499337638278


SIGNIFICANCE OF IMAGE PROCESSING IN MEDICAL INDUSTRY


Medical imaging is the procedure used to attain images of the body parts to diagnose, monitor, or treat medical conditions. There are millions of imaging procedures done every week worldwide. The images produced by medical equipment are composed of pixels, to which discrete brightness and color values are assigned. Through image processing, they can be efficiently processed, evaluated, and analyzed. Also, due to advancements in image processing tools, it has become possible to acquire high-quality images and analyze them using software, thereby facilitating the early detection of diseases like cancer and other abnormalities.

Technological advancements achieved in medical imaging over the last century created unprecedented opportunities for noninvasive diagnostics and established medical imaging as an integral part of healthcare systems today. One of the major areas of innovation representing these advancements is the interdisciplinary field of medical image processing.


We at MedCuore have curated a first-of-its-kind course designed with a complete focus on medical imaging using MATLAB.


To know more visit


AUTHOR

SHARANYA

PROJECT EXECUTIVE

MEDCUORE MEDICAL SOLUTIONS PVT LTD.


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