Mohd Yusoff Mashor, Prof. Dr.
http://dspace.unimap.edu.my:80/xmlui/handle/123456789/22960
2024-03-28T20:38:17ZAn Automated Segmentation and Counting of Ki67 Cells in Meningioma Using K-Means Clustering Technique
http://dspace.unimap.edu.my:80/xmlui/handle/123456789/69034
An Automated Segmentation and Counting of Ki67 Cells in Meningioma Using K-Means Clustering Technique
Fahmi Akmal, Dzulkifli; Mohd Yusoff, Mashor; Hasnan, Jaafar
Meningioma is a type of primary brain tumours. The meningiomas account for about one-third of all primary brain tumours. Image segmentation plays an important role in image analysis, especially detecting the tumours or cancerous areas in medical images. The output images from the segmentation prominently affect the system in detecting the tumour cells. Currently, the pathologists use the ‘eye-balling’ estimation technique to count the Ki67 cells. This technique was known as a time-saving measure. However, it has poor reliability and accuracy in counting the Ki67 cells. This paper proposed an automatic Ki67 cell counting in
meningioma by using k-means clustering approach. The k-means clustering was used to segment
the Ki67 cells and then the cells were classified into positive and negative Ki67 cells. The
proposed system has been tested on 12 histopathological meningioma images. The proposed
system is compared to the manually segmented images that have been validated in prior by the
pathologists. The results show that the proposed system was able to segment the Ki67 cells with
an average accuracy of 95.29%. The sensitivity and specificity of the proposed system were also
high with an average of 93.56% and 97.39%, respectively
Link to publisher's homepage at https://iopscience.iop.org/
2019-01-01T00:00:00ZClassification of Acute Leukemia Based on Multilayer Perceptron
http://dspace.unimap.edu.my:80/xmlui/handle/123456789/69028
Classification of Acute Leukemia Based on Multilayer Perceptron
Nurul Hazwani, Abd Halim; Mohd Yusoff, Mashor; Rosline, Hassan
In general, various artificial neural network have been applied in many areas such as modelling, pattern recognition, signal processing, diagnostic and prognostic. In this paper, artificial neural network are used to detect and classify the white blood cell (WBC) inside the acute leukemia blood samples. There are 25 features have been extracted from segmented
WBC, which consist of shape, color and texture based features. Then, it have been fed up as the neural network inputs for the classification process in order to classify the segmented regions into two classes either B or T. The training algorithm for MLP network is LevenbergMarquardt (LM). The MLP network achieves the highest testing accuracy of 96.99% for 4
hidden nodes at state of 5 by using the overall 25 input features. Thus, MLP network trained by
using LM algorithm is suitable for acute leukemia cells detection in blood sample.
Link to publisher's homepage at https://iopscience.iop.org/
2019-01-01T00:00:00ZSegmentation of Relevant Region in Breast Histopathology Images using FCM with Guided Initialization
http://dspace.unimap.edu.my:80/xmlui/handle/123456789/69025
Segmentation of Relevant Region in Breast Histopathology Images using FCM with Guided Initialization
Tan Xiao, Jian; Nazahah, Mustafa; Mohd Yusoff, Mashor; Khairul Shakir, Ab Rahman
This study proposes a modified initialization approach for the conventional FCM, namely FCM with guided initialization. The FCM with guided initialization was implemented to segment the relevant regions in the breast histopathology images. The initialization method to select initial centers is based on the Cyan (C) channel histogram. Area Overlap Measure (AOM) and Combined Equal Importance (CEI) were used to evaluate the performance of the proposed FCM with guided initialization. The obtained AOM and CEI for the overall dataset achieved promising results: 0.89 in AOM and 0.88 in CEI. When comparing the number of iterations required to complete the proposed FCM clustering algorithm, the FCM with guided initialization is found to be effective in reducing the search space by showing a lower number of iterations.
Link to publisher's homepage at https://iopscience.iop.org/
2019-01-01T00:00:00ZSegmentation of Irrelevant Regions using Color Thresholding Method: application in Breast Histopathology Images
http://dspace.unimap.edu.my:80/xmlui/handle/123456789/69024
Segmentation of Irrelevant Regions using Color Thresholding Method: application in Breast Histopathology Images
Tan Xiao, Jian; Nazahah, Mustafa; Mohd Yusoff, Mashor; Khairul Shakir, Ab Rahman
Segmentation of irrelevant regions in the breast histopathology image is essentially performed in preliminary or post processing stages. This study presents a color thresholding method to segment the irrelevant regions, specifically blood cells and hemorrhage in the RGB breast histopathology images. The conventional gray level global thresholding method was extended to color images by modifying the thresholding algorithm. The color thresholding method was performed by decomposing the RGB input image into coherent regions based on similarity in color feature. Area Overlap Measure (AOM) and Combined Equal Importance (CEI) are two statistical metrics used to evaluate the performance of the color thresholding method. The obtained AOM and CEI for the overall dataset in blood cells are 0.92 and 0.89, respectively. The color thresholding method is found to be effective and is able to produce plausible thresholding results in segmentation of irrelevant regions in RGB breast histopathology images.
Link to publisher's homepage at https://iopscience.iop.org/
2019-01-01T00:00:00Z