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Brain magnetic resonance image segmentation using novel improvement for expectation maximizing

Balafar MA, Ramli AR, Mashohor S

Neurosciences (Riyadh), 2011 Jul;16(3):242-7.
PMID: 21677615

To improve the quality of expectation maximizing (EM) for brain image segmentation, and to evaluate the accuracy of segmentation results.
A system of microcalcifications detection and evaluation of the radiologist: comparative study of the three main races in Malaysia

Al-Qdah M, Ramli AR, Mahmud R

Comput Biol Med, 2005 Dec;35(10):905-14.
PMID: 16310014

This paper uses wavelets in the detection comparison of breast cancer among the three main races in Malaysia: Chinese, Malays, and Indians followed by a system that evaluates the radiologist's findings over a period of time to gauge the radiologist's skills in confirming breast cancer cases. The db4 wavelet has been utilized to detect microcalcifications in mammogram-digitized images obtained from Malaysian women sample. The wavelet filter's detection evaluation was done by visual inspection by an expert radiologist to confirm the detection results of those pixels that corresponded to microcalcifications. Detection was counted if the wavelet-detected pixels corresponded to the radiologist's identified microcalcification pixels. After the radiologist's detection confirmation a new client-server radiologist recording and evaluation system is designed to evaluate the findings of the radiologist over some period of cancer detection working time. It is a system that records the findings of the Malaysian radiologist for the presence of breast cancer in Malaysian patients and provides a way of registering the progress of detecting breast cancer of the radiologist by tracking certain metric values such as the sensitivity, specificity, and receiver operator curve (ROC). The initial findings suggest that no single race mammograms are easier for wavelets' detections of microcalcifications and for the radiologist confirmation even though for this study the Chinese race samples detection average were a few percentages less than the other two races, namely the Malay and Indian races.
Wavelet-based compression of medical images: filter-bank selection and evaluation

Saffor A, bin Ramli AR, Ng KH

Australas Phys Eng Sci Med, 2003 Jun;26(2):39-44.
PMID: 12956184

Wavelet-based image coding algorithms (lossy and lossless) use a fixed perfect reconstruction filter-bank built into the algorithm for coding and decoding of images. However, no systematic study has been performed to evaluate the coding performance of wavelet filters on medical images. We evaluated the best types of filters suitable for medical images in providing low bit rate and low computational complexity. In this study a variety of wavelet filters are used to compress and decompress computed tomography (CT) brain and abdomen images. We applied two-dimensional wavelet decomposition, quantization and reconstruction using several families of filter banks to a set of CT images. Discreet Wavelet Transform (DWT), which provides efficient framework of multi-resolution frequency was used. Compression was accomplished by applying threshold values to the wavelet coefficients. The statistical indices such as mean square error (MSE), maximum absolute error (MAE) and peak signal-to-noise ratio (PSNR) were used to quantify the effect of wavelet compression of selected images. The code was written using the wavelet and image processing toolbox of the MATLAB (version 6.1). This results show that no specific wavelet filter performs uniformly better than others except for the case of Daubechies and bi-orthogonal filters which are the best among all. MAE values achieved by these filters were 5 x 10(-14) to 12 x 10(-14) for both CT brain and abdomen images at different decomposition levels. This indicated that using these filters a very small error (approximately 7 x 10(-14)) can be achieved between original and the filtered image. The PSNR values obtained were higher for the brain than the abdomen images. For both the lossy and lossless compression, the 'most appropriate' wavelet filter should be chosen adaptively depending on the statistical properties of the image being coded to achieve higher compression ratio.
Fulltext A novel N-input voting algorithm for X-by-wire fault-tolerant systems

Karimi A, Zarafshan F, Al-Haddad SA, Ramli AR

ScientificWorldJournal, 2014;2014:672832.
PMID: 25386613 DOI: 10.1155/2014/672832

Voting is an important operation in multichannel computation paradigm and realization of ultrareliable and real-time control systems that arbitrates among the results of N redundant variants. These systems include N-modular redundant (NMR) hardware systems and diversely designed software systems based on N-version programming (NVP). Depending on the characteristics of the application and the type of selected voter, the voting algorithms can be implemented for either hardware or software systems. In this paper, a novel voting algorithm is introduced for real-time fault-tolerant control systems, appropriate for applications in which N is large. Then, its behavior has been software implemented in different scenarios of error-injection on the system inputs. The results of analyzed evaluations through plots and statistical computations have demonstrated that this novel algorithm does not have the limitations of some popular voting algorithms such as median and weighted; moreover, it is able to significantly increase the reliability and availability of the system in the best case to 2489.7% and 626.74%, respectively, and in the worst case to 3.84% and 1.55%, respectively.
Fulltext Optimization of power utilization in multimobile robot foraging behavior inspired by honeybees system

Ahmad FA, Ramli AR, Samsudin K, Hashim SJ

ScientificWorldJournal, 2014;2014:153162.
PMID: 24949491 DOI: 10.1155/2014/153162

Deploying large numbers of mobile robots which can interact with each other produces swarm intelligent behavior. However, mobile robots are normally running with finite energy resource, supplied from finite battery. The limitation of energy resource required human intervention for recharging the batteries. The sharing information among the mobile robots would be one of the potentials to overcome the limitation on previously recharging system. A new approach is proposed based on integrated intelligent system inspired by foraging of honeybees applied to multimobile robot scenario. This integrated approach caters for both working and foraging stages for known/unknown power station locations. Swarm mobile robot inspired by honeybee is simulated to explore and identify the power station for battery recharging. The mobile robots will share the location information of the power station with each other. The result showed that mobile robots consume less energy and less time when they are cooperating with each other for foraging process. The optimizing of foraging behavior would result in the mobile robots spending more time to do real work.
Brain white and gray matter anatomy of MRI segmentation based on tissue evaluation

Salih QA, Ramli AR, Mahmud R, Wirza R

MedGenMed, 2005;7(2):1.
PMID: 16369380

Different approaches to gray and white matter measurements in magnetic resonance imaging (MRI) have been studied. For clinical use, the estimated values must be reliable and accurate when, unfortunately, many techniques fail on these criteria in an unrestricted clinical environment. A recent method for tissue clusterization in MRI analysis has the advantage of great simplicity, and it takes the account of partial volume effects. In this study, we will evaluate the intensity of MR sequences known as T1-weighted images in an axial sliced section. Intensity group clustering algorithms are proposed to achieve further diagnosis for brain MRI, which has been hardly studied. Subjective study has been suggested to evaluate the clustering group intensity in order to obtain the best diagnosis as well as better detection for the suspected cases. This technique makes use of image tissue biases of intensity value pixels to provide 2 regions of interest as techniques. Moreover, the original mathematic solution could still be used with a specific set of modern sequences. There are many advantages to generalize the solution, which give far more scope for application and greater accuracy.
Boosting diagnosis accuracy of Alzheimer's disease using high dimensional recognition of longitudinal brain atrophy patterns

Farzan A, Mashohor S, Ramli AR, Mahmud R

Behav Brain Res, 2015 Sep 1;290:124-30.
PMID: 25889456 DOI: 10.1016/j.bbr.2015.04.010

Boosting accuracy in automatically discriminating patients with Alzheimer's disease (AD) and normal controls (NC), based on multidimensional classification of longitudinal whole brain atrophy rates and their intermediate counterparts in analyzing magnetic resonance images (MRI).
Fulltext The tale of two vessels, vascular complications following a breach of the pelvic inner table due to acetabular screws: a report of two cases

Ayob KA, Merican AM, Sulaiman SH, Hariz Ramli AR

Jt Dis Relat Surg, 2021;32(1):239-244.
PMID: 33463444 DOI: 10.5606/ehc.2021.77862

Injuries to the pelvic vasculature during total hip arthroplasties are rare but have serious consequence. They demand urgent and early identification so that appropriate treatment can be instituted. If the bleeding is severe, cardiovascular compromise occurs intraoperatively and this will alert the surgeon of this possibility during acetabular screw placement. Alternatively, a delay in diagnosis can occur because the bleeding and the injured vessel are in the pelvic cavity and not visualized during the surgery. In this article, we report two cases from our center occurring within a six-month interval that sustained a vascular injury during acetabular drilling for screw placement for cementless cup fixation. Each case had a different vessel injury and different lessons can be learned from these rare injuries. The first case had an injury of the inferior gluteal artery following a breach of the sciatic notch. The vessel was treated with percutaneous embolization. The second case demonstrated a venous injury, following a medial protrusio technique for congenital hip dysplasia and a short anterosuperior screw, transecting the external iliac vein. This was subsequently repaired using an endovascular technique. We conclude the reasons for these vessel injuries after analyzing advanced imaging, discuss measures to avoid vessel injury and detail the minimally invasive method for their treatment.
Early Detection of Alzheimer's Disease Based on Clinical Trials, Three-Dimensional Imaging Data, and Personal Information Using Autoencoders

Akramifard H, Balafar MA, Razavi SN, Ramli AR

J Med Signals Sens, 2021 05 24;11(2):120-130.
PMID: 34268100 DOI: 10.4103/jmss.JMSS_11_20

Background: A timely diagnosis of Alzheimer's disease (AD) is crucial to obtain more practical treatments. In this article, a novel approach using Auto-Encoder Neural Networks (AENN) for early detection of AD was proposed.
Method: The proposed method mainly deals with the classification of multimodal data and the imputation of missing data. The data under study involve the MiniMental State Examination, magnetic resonance imaging, positron emission tomography, cerebrospinal fluid data, and personal information. Natural logarithm was used for normalizing the data. The Auto-Encoder Neural Networks was used for imputing missing data. Principal component analysis algorithm was used for reducing dimensionality of data. Support Vector Machine (SVM) was used as classifier. The proposed method was evaluated using Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Then, 10fold crossvalidation was used to audit the detection accuracy of the method.
Results: The effectiveness of the proposed approach was studied under several scenarios considering 705 cases of ADNI database. In three binary classification problems, that is AD vs. normal controls (NCs), mild cognitive impairment (MCI) vs. NC, and MCI vs. AD, we obtained the accuracies of 95.57%, 83.01%, and 78.67%, respectively.
Conclusion: Experimental results revealed that the proposed method significantly outperformed most of the stateoftheart methods.
Improvement of digital mammogram images using histogram equalization, histogram stretching and median filter

Langarizadeh M, Mahmud R, Ramli AR, Napis S, Beikzadeh MR, Rahman WE

J Med Eng Technol, 2011 Feb;35(2):103-8.
PMID: 21204610 DOI: 10.3109/03091902.2010.542271

Breast cancer is one of the most important diseases in females worldwide. According to the Malaysian Oncological Society, about 4% of women who are 40 years old and above are involved have breast cancer. Masses and microcalcifications are two important signs of breast cancer diagnosis on mammography. Enhancement techniques, i.e. histogram equalization, histogram stretching and median filters, were used to provide better visualization for radiologists in order to help early detection of breast abnormalities. In this research 60 digital mammogram images which includes 20 normal and 40 confirmed diagnosed cancerous cases were selected and manipulated using the mentioned techniques. The original and manipulated images were scored by three expert radiologists. Results showed that the selected methods have a positive significant effect on image quality.
Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review

Jalalian A, Mashohor SB, Mahmud HR, Saripan MI, Ramli AR, Karasfi B

Clin Imaging, 2013 May-Jun;37(3):420-6.
PMID: 23153689 DOI: 10.1016/j.clinimag.2012.09.024

Breast cancer is the most common form of cancer among women worldwide. Early detection of breast cancer can increase treatment options and patients' survivability. Mammography is the gold standard for breast imaging and cancer detection. However, due to some limitations of this modality such as low sensitivity especially in dense breasts, other modalities like ultrasound and magnetic resonance imaging are often suggested to achieve additional information. Recently, computer-aided detection or diagnosis (CAD) systems have been developed to help radiologists in order to increase diagnosis accuracy. Generally, a CAD system consists of four stages: (a) preprocessing, (b) segmentation of regions of interest, (c) feature extraction and selection, and finally (d) classification. This paper presents the approaches which are applied to develop CAD systems on mammography and ultrasound images. The performance evaluation metrics of CAD systems are also reviewed.
Fulltext Methods and Challenges in Shot Boundary Detection: A Review

Abdulhussain SH, Ramli AR, Saripan MI, Mahmmod BM, Al-Haddad SAR, Jassim WA

Entropy (Basel), 2018 Mar 23;20(4).
PMID: 33265305 DOI: 10.3390/e20040214

The recent increase in the number of videos available in cyberspace is due to the availability of multimedia devices, highly developed communication technologies, and low-cost storage devices. These videos are simply stored in databases through text annotation. Content-based video browsing and retrieval are inefficient due to the method used to store videos in databases. Video databases are large in size and contain voluminous information, and these characteristics emphasize the need for automated video structure analyses. Shot boundary detection (SBD) is considered a substantial process of video browsing and retrieval. SBD aims to detect transition and their boundaries between consecutive shots; hence, shots with rich information are used in the content-based video indexing and retrieval. This paper presents a review of an extensive set for SBD approaches and their development. The advantages and disadvantages of each approach are comprehensively explored. The developed algorithms are discussed, and challenges and recommendations are presented.
Computer-Assisted Diagnosis System for Breast Cancer in Computed Tomography Laser Mammography (CTLM)

Jalalian A, Mashohor S, Mahmud R, Karasfi B, Iqbal Saripan M, Ramli AR

J Digit Imaging, 2017 Dec;30(6):796-811.
PMID: 28429195 DOI: 10.1007/s10278-017-9958-5

Computed tomography laser mammography (Eid et al. Egyp J Radiol Nucl Med, 37(1): p. 633-643, 1) is a non-invasive imaging modality for breast cancer diagnosis, which is time-consuming and challenging for the radiologist to interpret the images. Some issues have increased the missed diagnosis of radiologists in visual manner assessment in CTLM images, such as technical reasons which are related to imaging quality and human error due to the structural complexity in appearance. The purpose of this study is to develop a computer-aided diagnosis framework to enhance the performance of radiologist in the interpretation of CTLM images. The proposed CAD system contains three main stages including segmentation of volume of interest (VOI), feature extraction and classification. A 3D Fuzzy segmentation technique has been implemented to extract the VOI. The shape and texture of angiogenesis in CTLM images are significant characteristics to differentiate malignancy or benign lesions. The 3D compactness features and 3D Grey Level Co-occurrence matrix (GLCM) have been extracted from VOIs. Multilayer perceptron neural network (MLPNN) pattern recognition has developed for classification of the normal and abnormal lesion in CTLM images. The performance of the proposed CAD system has been measured with different metrics including accuracy, sensitivity, and specificity and area under receiver operative characteristics (AROC), which are 95.2, 92.4, 98.1, and 0.98%, respectively.
Fulltext Supervised retinal vessel segmentation from color fundus images based on matched filtering and AdaBoost classifier

Memari N, Ramli AR, Bin Saripan MI, Mashohor S, Moghbel M

PLoS One, 2017;12(12):e0188939.
PMID: 29228036 DOI: 10.1371/journal.pone.0188939

The structure and appearance of the blood vessel network in retinal fundus images is an essential part of diagnosing various problems associated with the eyes, such as diabetes and hypertension. In this paper, an automatic retinal vessel segmentation method utilizing matched filter techniques coupled with an AdaBoost classifier is proposed. The fundus image is enhanced using morphological operations, the contrast is increased using contrast limited adaptive histogram equalization (CLAHE) method and the inhomogeneity is corrected using Retinex approach. Then, the blood vessels are enhanced using a combination of B-COSFIRE and Frangi matched filters. From this preprocessed image, different statistical features are computed on a pixel-wise basis and used in an AdaBoost classifier to extract the blood vessel network inside the image. Finally, the segmented images are postprocessed to remove the misclassified pixels and regions. The proposed method was validated using publicly accessible Digital Retinal Images for Vessel Extraction (DRIVE), Structured Analysis of the Retina (STARE) and Child Heart and Health Study in England (CHASE_DB1) datasets commonly used for determining the accuracy of retinal vessel segmentation methods. The accuracy of the proposed segmentation method was comparable to other state of the art methods while being very close to the manual segmentation provided by the second human observer with an average accuracy of 0.972, 0.951 and 0.948 in DRIVE, STARE and CHASE_DB1 datasets, respectively.
Fulltext A case of metastatic adenocarcinoma from an unknown primary involving the aortic bifurcation

Safri LS, Md Noh MSF, Hariz Ramli AR, Md Pauzi SH, Md Idris MA, Harunarashid H

J Vasc Surg Cases Innov Tech, 2018 Jun;4(2):160-162.
PMID: 29942910 DOI: 10.1016/j.jvscit.2018.03.004

Aortic malignant neoplasms are rare; these may be primary or secondary (metastatic). Increasing use of cross-sectional imaging has allowed better detection and diagnosis of these conditions. We encountered a young woman presenting with acute abdomen who was found on cross-sectional imaging to have a malignant tumor involving the aortic bifurcation. An en bloc excision of the tumor was performed, with distal aorta reconstruction using an aortoiliac Dacron graft; this was complicated with infection and graft occlusion, necessitating total removal and extra-anatomic bypass. A pathologic diagnosis of metastatic adenocarcinoma involving the aortic bifurcation, with an unknown primary, was made.
Towards low carbon society in Iskandar Malaysia: Implementation and feasibility of community organic waste composting

Bong CP, Goh RKY, Lim JS, Ho WS, Lee CT, Hashim H, et al.

J Environ Manage, 2017 Dec 01;203(Pt 2):679-687.
PMID: 27267145 DOI: 10.1016/j.jenvman.2016.05.033

Rapid population growth and urbanisation have generated large amount of municipal solid waste (MSW) in many cities. Up to 40-60% of Malaysia's MSW is reported to be food waste where such waste is highly putrescible and can cause bad odour and public health issue if its disposal is delayed. In this study, the implementation of community composting in a village within Iskandar Malaysia is presented as a case study to showcase effective MSW management and mitigation of GHG emission. The selected village, Felda Taib Andak (FTA), is located within a palm oil plantation and a crude palm oil processing mill. This project showcases a community-composting prototype to compost food and oil palm wastes into high quality compost. The objective of this article is to highlight the economic and environment impacts of a community-based composting project to the key stakeholders in the community, including residents, oil palm plantation owners and palm oil mill operators by comparing three different scenarios, through a life cycle approach, in terms of the greenhouse gas emission and cost benefit analysis. First scenario is the baseline case, where all the domestic waste is sent to landfill site. In the second scenario, a small-scale centralised composting project was implemented. In the third scenario, the data obtained from Scenario 2 was used to do a projection on the GHG emission and costing analysis for a pilot-scale centralised composting plant. The study showed a reduction potential of 71.64% on GHG emission through the diversion of food waste from landfill, compost utilisation and significant revenue from the compost sale in Scenario 3. This thus provided better insight into the feasibility and desirability in implementing a pilot-scale centralised composting plant for a sub-urban community in Malaysia to achieve a low carbon and self-sustainable society, in terms of environment and economic aspects.

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