magnetic resonance imaging is discussed in this presentation in an easy way.

1. APPLICATIO
N OF MRI
By:
MishkaatJamil(20BME023)
AfnanQureshi(20BME002)

2. Jens Martensson
Segmentation Techniques
For Tissue Differentiation
In MRI Of Ophthalmology
Using Fuzzy Clustering
Algorithms
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3. Jens Martensson
Case Study
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• Patient: 2-year-old girl with Retinoblastoma in her
left eye.
• Imaging: CT scan revealed a large tumor; MRI
obtained for detailed assessment.
• MRI Findings: Tumor mass in left eye, high T1
signal, low T2 signal, optic nerve abnormalities.
• MRI Acquisition: High-resolution transaxial
imaging with optimized parameters.
• Analysis: Unsupervised fuzzy clustering to aid
diagnosis.
• Purpose: Enhance diagnosis with advanced
imaging analysis.

4. Jens Martensson
MRI Application
1. MRI in Ophthalmology Diagnosis:
• MRI was used in the diagnosis of Retinoblastoma, an inborn
oncological disease in Ophthalmology, showing symptoms in early
childhood.
• It helped detect abnormal tissues, such as intraocular tumors,
optic nerve abnormalities, and tumor invasion, aiding in early
treatment planning.
2. MRI Segmentation Techniques:
• FCM and AFCM segmentation techniques were applied to
differentiate tissues in Ophthalmological MRIs.
• These techniques provided useful information for tissue
classification, especially in identifying tumor tissues and
abnormalities in the optic nerve and chiasma.
3. Enhancing Image Analysis:
• MRI segmentation and clustering algorithms were used to
enhance image analysis and improve the detection of small
tumors.
• Window segmentation was employed to focus on specific areas of
interest, such as tumor tissues, connective tissues, and nervous
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4

5. Jens Martensson
Fuzzy Clustering Algorithms For Segmentation
FCM
• Soft Segmentation Pixels can belong to
multiple tissues with varied membership
• Uses fixed fuzziness parameter for MRI
pixel segmentation.
• Limited accuracy in capturing subtle
tissue variations in MRI.
• Suitable for standard MRI segmentation
tasks.
• Requires manual parameter adjustments
for optimal results.
AFCM
• Robust Segmentation AFCM overcomes
limitations of uniform fuzziness settings
in FCM
• Dynamically adjusts fuzziness parameter
during MRI pixel segmentation.
• Enhances accuracy, especially in
differentiating tissues with varying
densities.
• Tailored for MRI segmentation, adapting
to local tissue characteristics.
• Automated parameter adaptation
reduces the need for manual
adjustments, improving efficiency.
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6. Jens Martensson
Results
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The FCM and AFCM clustering algorithms are used to determine the actual
symptoms of Retinoblastoma from MRI and determine if further treatment is
necessary. Since glioblastoma is hereditary, children whose families have a history of
this problem can undergo periodic diagnostic tests with MRI using the FCM and
AFCM algorithms to detect the smallest sign symptoms for early detection and
treatment.

7. Jens Martensson 7
Thank You