Open AccessA New Image Segmentation of Leptomeningeal Metastasis in Leukemia Patients
Author(s) -
H. Z. Ilmadina,
A. M. Arymurthy,
Rosalina Rosalina
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1577/1/012014
Subject(s) - medicine , segmentation , leukemia , magnetic resonance imaging , radiology , metastasis , malignancy , myeloid leukemia , cancer , pathology , artificial intelligence , computer science
Leptomeningeal metastasis is an indication of the malignancy that occurs in leukemia patients. Although it only has a 5-10% portion caused the leukemia patient to relapse, the abnormality is the basis in determining the best treatment given to them. Leptomeningeal metastasis are better detected by using Magnetic Resonance Imaging (MRI) because of their high sensitivity in neuraxis images. High ability to see and analyze is needed for a radiologist in reading the Brain MRI results of leukemia patients with suspect leptomeningeal metastasis. Therefore, the classification will take a long time and allow for the misreading of the results. In this experiment, we used a dataset from the Brain MRI of leukemia patients of Dharmais Cancer Hospital. We implemented the proposed method in performing the leptomeningeal metastasis segmentation. The preprocessing image applied for sharpening and removing unwanted noises in the image using the Median Filter. A hybrid semi-automated skull stripping was also developed to improve the accuracy of the segmentation. Then Fuzzy C-Means is used to segment the abnormalities and reach an average evaluation performance at 49.1% Jaccard Index.