Answer to Question #227422 in UNIX/Linux Programming for Chemistry student

Question

Zaid is an IT expert in a medical company that works on brain tumor detection. Zaid needs to transform the brain image scanned through monochromic camera for image analyses.

What will you suggest Zaid from 2D transformation and 3D transformation of monochrome image in the transformation process? Discuss it with solid reason.

Answer 1

I would suggest a 3D transformation of the monochrome image in the transformation process.

Because

3D Segmentation

With 3D image segmentation, data acquired from 3D imaging modalities such as Computed Tomography (CT), Micro-Computed Tomography (micro-CT or X-ray) or Magnetic Resonance Imaging (MRI) scanners is labeled to isolate regions of interest. These regions represent any subject or sub-region within the scan that will later be scrutinized. This could facilitate analysis of part of the human body, or a specific feature within an industrial component or assembly.

Uses of 3D Segmentation

1. MRI scans were obtained from an adult female brain

2. Data is imported to Simple ware ScanIP for 3D image segmentation of the brain and skull, including tissue, cerebellum, skin, and skull areas.

3. FE meshes were generated using Simple ware software to convert the complex segmented image data into a volumetric mesh including tissues, features, and color mapping is used to quantify the battery.

4. Craniotomies were simulated using SIMULIA Abacus to study different swelling scenarios, with the goal of identifying an optimal opening size to control pressure and minimize the risk of axonal damage.

5. Future work will focus on improving the simulation of these high-risk surgeries to inform pre-surgical planning and improve patient care.

 

 

Tumor detection is a challenging task due to the overlap in intensity between tumor and normal tissue, the deformation of nearby healthy tissues, and the large heterogeneity of tumors in terms of shape, position, size, and appearance

In order to detect 3D-MRI brain tumors accurately, contours should be determined correctly. To achieve this goal, the proposed model fuses semantically k-means and DA optimization. Herein, the semantic fusion is achieved using k-means inside DA clustering, i.e., rather than using a random center of the mass of the neighborhood (cluster centers) within DA clustering, k-means regulate these accurately.

A variety of approaches have been attempted to tackle the problem of brain tumor segmentation of 3D MRI. Two methods have been proposed to deal with volumetric input. The first of these uses the idea of natural image segmentation, where the 3D volume is cut into 2D slices, and a 2D network is then trained to process each slice individually or sequentially. The second method involves cutting the volume into patches, then training a 3D network to process these patches. In the following stage, the two methods use a sliding window to test the original volume. Both methods have advantages and disadvantages. Due to varying resolutions in the third dimension of the MRI dataset, 3D-MRI images are converted into 2D slices. Within the second category, level set-based segmentation is broadly used. The level set method provides a direct way to estimate the geometric properties of the evolving structure.