The Vital Role of Medical Image Processing in Diagnosis and Treatment
The Vital Role of Medical Image Processing in Diagnosis and Treatment
February 23, 2024
Medical Devices
Introduction:
Medical Image processing becomes a major part of medical software development and applications domain, considering its great effect on diagnosis and treatment of a lot of medical conditions. Medical Image processing starts with the storing the data acquired from the patient through the imaging device in a digital form and goes on to the enhancement process which aims to prepare the data for further operations of one or more of the different types of processing such Medical image analysis. The enhancement process could be conducted just to improve the visual content of the image.
Abstract:
Unfortunately, in many cases, the obtained images have a low quality due to sever condition, bad data acquisition techniques, improper or low-quality hardware of the imaging device. Since there is a great reliance on these images as important evidence for diagnosis and treatment these problems yell a great need of software solutions to enhance the image.
| Figure1: Examining Techniques for Enhancing Images in Medical Imaging |
Medical image enhancement:
Medical image enhancement techniques, which are considered as low-level image processing, could participate directly in aiding health care professionals. These techniques aim to improve the overall look and clarity of the image to make easier to deal with it by visual inspection. These techniques also set the stage to the application of image Analyzing techniques.
One of the global most popular techniques of image enhancement are called histogram equalization.
Image enhancement is the process of adjusting digital images so that the results are more suitable for display or further image analysis
Edward J. Delp
What is the image histogram? and why do we need it
Histogram of an image is a form that represents the data of the image as graph of tow axis, one (X-axis) for values of the pixels which represents the gray-level or color-level in RGB (intensity of the pixel), the other axis (Y-axis) the height of the point corresponding with the intensity below represents the number of pixels which have this value.
When the values of the pixels are relatively close to each other, the image may seem too dark or too bright and could be nearly impossible to differentiate between the objects in the image accurately by visual inspection and even harder to get accurate results using image analyzing software. In this case the histogram of the image will show narrow distribution in a small range of the X- axis.
Here we need the equalization of this histogram which means giving new values for the pixels in the image and stretching out this narrow distribution into wider range with keeping the relationship between pixels in mind to produce clearer image with better contrast
| Figure 2: A medical image and its intensity histogram. (A) Original image. (B) Normalized intensity histogram |
How is it performed?
The histogram equalization could be performed easily in many programming languages which has a special libraries for such missions like Python with openCV library, R language with imager package and it is popular in Matlab language using “hesteq()” code for images in grayscale with a lot of related functionality to modify how the code works depending on the image and the requirement of the process.
The usage of one language or another depends on the system requirements mainly in terms of efficiency and speed.
Types of histogram equalization:
However, there are different types of histogram equalization.
- Global Histogram Equalization (GHE): in this type the whole image histogram is stretched out in the same way for all the image parts
- Adaptive Histogram Equalization (AHE): This type is different than the previous one that the image is divided into many local regions and every region’s histogram is equalized on its own.
- Contrast Limited Adaptive Histogram Equalization (CLAHE): in this one the histogram for every local region of the image is equalized independently with a limitation that are chooses manually, along with the size of the local window
- Histogram Specification (HS): In this type the user chooses manually the demanded histogram of the resulted image.
- Brightness Preserving Dynamic Histogram Equalization (BPDHE): This type is designed to keep the mean brightness of the source image as possible. Where the resulted image mean brightness of other techniques differs a lot from the mean brightness of the source image.
The usage of one method or another depends on the wanted result and the source image itself
Conclusion:
Overcoming challenges in medical device distribution requires robust strategies for managing regulatory changes, supply chain disruptions, technological advancements, and market demands. Adapting swiftly and efficiently while maintaining quality and compliance is key to ensuring uninterrupted supply and high standards of healthcare delivery.
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