1. Kampus
Kejuruteraan
Engineering
Campus
AUTOMATED MULTIMODAL WHITE MATTER HYPERINTENSITIES IDENTIFICATION USING TWO-TIER SEGMENTATION TECHNIQUE FOR MRI BRAIN IMAGE
Iza Sazanita Isa1*, Dr Siti Noraini Sulaiman1, Prof Nooritawati Md2 Tahir, PM Dr Muzaimi Mustapha3, Dr Noor Khairiah A. Karim4
1Faculty of Electrical Engineering, Universiti Teknologi MARA, Penang Campus
13500 Permatang Pauh, Pulau Pinang.
2Faculty of Electrical Engineering, Universiti Teknologi MARA,
43200 Shah Alam, Selangor.
3School of Medical Sciences, Universiti Sains Malaysia, Health Campus Kubang Kerian, Kelantan.
4Advanced Medical And Dental Institute, Universiti Sains Malaysia, Bertam, Kepala Batas, Pulau Pinang.

2. Introduction 1
Small regions of high signal intensity appear on (MRI) images.
Risk indicator for several chronic diseases including stroke, Alzheimer, dementia and cardiovascular risk.
The quantification method for WMH identification on MRI images is important to accurately analyze and interpret these images.
There are various methods have been proposed either manual, semiautomatic or automatic approach to segment the WMH of the MRI images.
In this study, an automatic WMH identification is introduced to overcome the presence of any limitation by using MRI images of T2-WI and FLAIR images.
WMH
FLAIR
T2-WI

3. Mri Image PROCESSING techniques2
Based on the fact that clinical image processing and computer-aided WM delineation is effective in facilitating the radiologist to quantify WM lesion particularly the WMH
Provide better input for image processing
For radiologists’ perception
Improve interpretability
Information retrieval
MRI Image processing:
most commonly used MRI sequences for brain image segmentation are T1-WI, T2-WI and FLAIR.

4. Problem statement 3
Researchers have found that there is no standard method for quantifying the WM changes which lead to several risks of chronic diseases.
Up until now, the quantification of WMH delineation in clinical site is still performed manually. The affected region of WM interpretation and analysis has been done by a few certified radiologists.
Manual delineation method are potentially easier to use by medical expert but do not providing greater details and reliability.
Various clinical rating scale have been developed to quantify the expression of WM, but different methods deliver various degrees of reliability.
Semi- automated methods requiring human inputs are subjective, time consuming, laborious, error prone and vulnerable to intra- and inter-rater variability.
The semi-automated method provides thorough details as well but not applicable on wide clinical analysis.

5. Research Objective Objective of this research:4
Objective of this research:
To developed a new automated technique for detection WMH in different MRI images based on multimodal processing techniques.
To enhanced the automated technique for detection WMH by developing new algorithm for image enhancement and segmentation techniques to apply in (1).
To classify the severity of quantified WMH based on measured features of WMH identified in (3).

6. Methodology 5 Multimodal Image processing techniques:
Enhancement
Directly deals with pixels value
Manipulate pixels value to achieve desire enhancement
Enhance FLAIR images to enhance WMH contrast
Segmentation
Clustering the brain region based on same classes
1st segmentation: Clustering into 4 class (WM, GM, CSF & Background)
2nd segmentation: Clustering into 3 class (WM, WMH & Background)
Develop new automated WMH detection method using multimodal techniques

7. Methodology 6
An overview of proposed automatic WMH identification and classification scheme

8. Methodology : Preprocessing7
Preprocessing stage:
1. Skull and Scalp Removal – morphological operations.
This process will further simplify the clustering process in the image segmentation stage.
2. Filtering – Median filter
To remove noise and inhomogeneity correction tool to improve the accuracy of MRI images for other processing step.

9. Methodology: 2nd Stage 10
Segmentation phase:
Enhancement phase:

10. Methodology: Final Stage11
2nd stage
Final stage

11. Automated segmentation
Results 12
Manual delineation by radiologist
Automated segmentation
Qualitative results on WMH Manual delineation and automated detection

12. Results 13
Higher correlation analysis (>80%) between automated technique to manual method.

13. Results 14
Data set
Subjects number
Test
Volume different
DCS
TPR
PPV
IPPT 10
Training
56.01
0.33
39.92
26.76
Testing
43.05
0.36
61.57
26.77
2015 Grand Challenge
Validation (Rater 1)
44.47
0.69
0.64
0.49
Validation (Rater 2)
29.87
0.55
0.52
33.12
0.75
0.79
33.09
0.72
0.74
Comparison results of similarity index based on different dataset of images.

14. Results 15
Results of FLAIR image contrast by using proposed enhancement algorithm and WMH masking on T2-WI

15. Results 16 no. HE AHE/CLAHE Proposed method PSNR Average Gradient 1
0.0013
0.0010
0.0007 2
0.0014
0.0008 3
0.0012
0.0005 4
0.0020 5
0.0017
0.0011
0.0009 6 7 8 9
0.0016 10
0.0015

16. Results 17 WMH severity classification:
This indicates that this system is highly significant for classifying focal and large WMH but need some improvement in punctuate classification

17. Conclusion 18
In conclusion, this automated method succeeded in automatically detecting WMH region in the MRI brain images by using multimodal processing techniques with new enhancement algorithm scheme and also capable to classify the severity of WMH into focal and large sizes.

18. Novelty 19
Results and findings from this research can serve as a simple indicator for image processing technique, leading to great opportunity for new method of image analysis in the future. In addition, this research may also serve as computational aided tool for radiologist to help them in identification and analyzing the WMH and its severity particularly in clinical diagnosis.

19. Commercialization potential/Benefit to community20
This automated WMH detection system is highly potential to commercialize since it consists a new innovated method for MWH identification and classification to determine its severity. The proposed method is fully automate and may serve as several brain disease indicator in medical diagnosis and analysis.
The applicability of this automated system can ensure the producing of standardized and better images quality and thus would help the radiologist make an accurate clinical diagnosis to patients as well as reducing false diagnosis.
It also applicable at radiology department at the hospitals (for Medical Imaging analysis) particularly for MRI analysis. This also would assist the image analysts and scientist to analyze images more easily despite prone to any human error.

20. INTELECTUAL PROPERTIES21
Automatic Detection of White Matter Hyperintensities (WMH) Severity – No. CRLY003457, Intellectual Property Corporation of Malaysia.
A Method and System for White Matter Hyperintensities Identification and Classification – No. PI , Intellectual Property Corporation of Malaysia.

21. Publications 22
Iza Sazanita Isa, Siti Noraini Sulaiman and Muzaimi Mustapha, “The T2-weighted MRI Images Via Automated Segmentation Techniques Using K-means Clustering And Otsu-based Thresholding Method”, Jurnal Teknologi (Sciences & Engineering), vol.77(2), pp. 1-6 (2015). Scopus indexed, Status: Published.
Iza Sazanita Isa, Siti Noraini Binti Sulaiman, Muzaimi Mustapha and Sailudin Darus, “Evaluating Denoising Performances Of Fundamental Filters For T2-weighted Mri Images”, Procedia Computer Science, Elsevier, vol.60, pp , (2015). Scopus indexed, Status: Published.
Iza Sazanita, Siti Noraini Sulaiman and Muzaimi Mustapha, “Fundamental Filters Performances Based on Window Sizes for T2-Weighted MRI Images”, International Journal of Simulation: Systems, Science and Technology, 16(4), pp. 71 – 76, (2015). Scopus indexed, Status: Published.
Iza Sazanita, Siti Noraini Sulaiman, Muzaimi Mustapha and Noor Khairiah A. Karim, “ WMH Detection Using Improved AIR-AHE-Based Algorithm for Two-tier Segmentation Technique”, Regional Conference on Science Technology and Science Social (RCSTSS2016), Cameron Highland, Malaysia, 4 – 6 Dec Status: Accepted and will be published in Malaysian Journal of Analytical Sciences (Scopus index), chapter in a book published by Springer or selected journals.
Iza Sazanita Isa, Siti Noraini Binti Sulaiman, Muzaimi Mustapha and Sailudin Darus, “Automatic contrast enhancement of brain MR images using Average Intensity Replacement based on Adaptive Histogram Equalization (AIR-AHE)”, Biocybernetics and Biomedical Engineering, vol. 37 (1), pp. 24 – 34, (2017). Impact Factor: 0.808, Status: Published.
Iza Sazanita Isa, Siti Noraini Sulaiman and Muzaimi Mustapha, “The T2-weighted MRI Images Via Automated Segmentation Techniques Using K-means Clustering And Otsu-based Thresholding Method”, Jurnal Teknologi (Sciences & Engineering), vol.77(2), pp. 1-6 (2015). Scopus indexed, Status: Published.
Iza Sazanita Isa, Siti Noraini Binti Sulaiman, Muzaimi Mustapha and Sailudin Darus, “Evaluating Denoising Performances Of Fundamental Filters For T2-weighted Mri Images”, Procedia Computer Science, Elsevier, vol.60, pp , (2015). Scopus indexed, Status: Published.
Iza Sazanita, Siti Noraini Sulaiman and Muzaimi Mustapha, “Fundamental Filters Performances Based on Window Sizes for T2-Weighted MRI Images”, International Journal of Simulation: Systems, Science and Technology, 16(4), pp. 71 – 76, (2015). Scopus indexed, Status: Published.
Iza Sazanita, Siti Noraini Sulaiman, Muzaimi Mustapha and Noor Khairiah A. Karim, “ WMH Detection Using Improved AIR-AHE-Based Algorithm for Two-tier Segmentation Technique”, Regional Conference on Science Technology and Science Social (RCSTSS2016), Cameron Highland, Malaysia, 4 – 6 Dec Status: Accepted and will be published in Malaysian Journal of Analytical Sciences (Scopus index), chapter in a book published by Springer or selected journals.
Iza Sazanita Isa, Siti Noraini Binti Sulaiman, Muzaimi Mustapha and Sailudin Darus, “Automatic contrast enhancement of brain MR images using Average Intensity Replacement based on Adaptive Histogram Equalization (AIR-AHE)”, Biocybernetics and Biomedical Engineering, vol. 37 (1), pp. 24 – 34, (2017). Impact Factor: 0.808, Status: Published.

22. Grants 23
New ROI - based Features Extraction Method Based on White Matter Lesions from MRI Images of Small Vessel Stroke Predisposition (RM97500), (FRGS).
Duration: 2 Nov 2015 – 31 Oct 2018

23. Awards 24
Best Paper Award at IEEE-EMBS Conference on Biomedical Engineering and Science (IECBES2016), Kuala Lumpur, Malaysia, 4th – 6th Dec 2016.
Gold Medal at Invention, Innovation & Design Exposition 2016 (IIDEX 2016), UiTM Shah Alam, 20 – 23 September 2016.
Gold Medal at International Invention & Innovative Competition Series 1/2016 (InIIC 1/2016), Port Dickson, Negeri Sembilan, 28 May 2016.

24. Thank You
Iza Sazanita Isa1*, Dr Siti Noraini Sulaiman1, Prof Nooritawati Md2 Tahir, PM Dr Muzaimi Mustapha3, Dr Noor Khairiah A. Karim4
1Faculty of Electrical Engineering, Universiti Teknologi MARA, Penang Campus
13500 Permatang Pauh, Pulau Pinang.
2Faculty of Electrical Engineering, Universiti Teknologi MARA,
43200 Shah Alam, Selangor.
3School of Medical Sciences, Universiti Sains Malaysia, Health Campus Kubang Kerian, Kelantan.
4Advanced Medical And Dental Institute, Universiti Sains Malaysia, Bertam, Kepala Batas, Pulau Pinang.