Papers by Akila Subasinghe
IEEE Access
Pupil localization extracts pupil center coordinates from images and videos of the human eye alon... more Pupil localization extracts pupil center coordinates from images and videos of the human eye along with the pupillary boundary. Pupil localization essentially plays a major role in identity verification, disease recognition, visual focus of attention (VFOA) tracking, dementia cognitive assessment, and human fatigue detection. However, the process of pupil localization still remains challenging due to various factors, such as poor-quality images, eye makeup, contact lenses, eyelashes, hair strips, eyebrows, closed eyes, and eye saccades. The pupil localization strategies are essentially divided into learning-based and non-learningbased approaches and discussed in detail with the relevant techniques used. This article aims to deliver the essence of current trends in pupil localization and critically discuss the advantages and disadvantages of each method. Hence, this article can be useful to a broad spectrum of readers as a guide to analyzing the latest trends in pupil localization. INDEX TERMS Eye and face databases, eye tracking, gaze tracking, iris recognition, learning-based methods, non-learning-based methods, pupil localization.
F1000Research, Jul 1, 2016
Accurate detection of the human metaphase chromosome centromere is a critical element of cytogene... more Accurate detection of the human metaphase chromosome centromere is a critical element of cytogenetic diagnostic techniques, including chromosome enumeration, karyotyping and radiation biodosimetry. Existing centromere detection methods tends to perform poorly in the presence of irregular boundaries, shape variations and premature sister chromatid separation. We present a centromere detection algorithm that uses a novel contour partitioning technique to generate centromere candidates followed by a machine learning approach to select the best candidate that enhances the detection accuracy. The contour partitioning technique evaluates various combinations of salient points along the chromosome boundary using a novel feature set and is able to identify telomere regions as well as detect and correct for sister chromatid separation. This partitioning is used to generate a set of centromere candidates which are then evaluated based on a second set of proposed features. The proposed algorithm outperforms previously published algorithms and is shown to do so with a larger set of chromosome images. A highlight of the proposed algorithm is the ability to rank this set of centromere candidates and create a centromere confidence metric which may be used in post-detection analysis. When tested with a larger metaphase chromosome database consisting of 1400 chromosomes collected from 40 metaphase cell images, the proposed algorithm was able to accurately localize 1220 centromere locations yielding a detection accuracy of 87%.
A comprehensive survey on the applications of machine learning techniques on maritime surveillance to detect abnormal maritime vessel behaviors
WMU Journal of Maritime Affairs
Automated segmentation of standard scanning planes to measure biometric parameters in foetal ultrasound images – a survey
Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization
Mechatronics in Landmine Detection and Removal
Mechanical Engineering Series, 2007
Proc. of the International Conference on Information and Automation (IEEE technical co-sponsor), Dec 15, 2005
Abstract–Deployment of robots for humanitarian demining programs in former tropical battlefields ... more Abstract–Deployment of robots for humanitarian demining programs in former tropical battlefields has been a significant challenge due to the very complexity of the unstructured vegetated environments and the terrain conditions found in most tropical minefields. This makes conventional navigation algorithms with high priority to collision avoidance, inappropriate for this application. This paper presents a simple statistical approach to behavior planning for fully embedded mobile robots navigating in vegetated environments ...
2022 2nd International Conference on Advanced Research in Computing (ICARC), 2022
Diagnosing and treating lung diseases can be challenging since the signs and symptoms of a wide r... more Diagnosing and treating lung diseases can be challenging since the signs and symptoms of a wide range of medical conditions can indicate interstitial lung diseases. Respiratory diseases impose an immense worldwide health burden. It is even more deadly when considering COVID-19 in present times. Auscultation is the most common and primary method of respiratory disease diagnosis. It is known to be non-expensive, non-invasive, safe, and takes less time for diagnosis. However, diagnosis accuracy using auscultation is subjective to the experience and knowledge of the physician, and it requires extensive training. This study proposes a solution developed for respiratory disease diagnosis. 'Smart Stethoscope' is an intelligent platform for providing assistance in respiratory disease diagnosis and training of novice physicians, which is powered by state-of-the-art artificial intelligence. This system performs 3 main functions(modes). These 3 modes are a unique aspect of this study. The real-time prediction mode provides real-time respiratory diagnosis predictions for lung sounds collected via auscultation. The offline training mode is for trainee doctors and medical students. Finally, the expert mode is used to continuously improve the system's prediction performance by getting validations and evaluations from pulmonologists. The smart stethoscope's respiratory disease diagnosis prediction model is developed by combining a state-of-the-art neural network with an ensembling convolutional recurrent neural network. The proposed convolutional Bi-directional Long Short-Term Memory (C- Bi LSTM) model achieved an accuracy of 98% on 6 class classification of breathing cycles for ICBHF17 scientific challenge respiratory sound database. The novelty of the project lies on the whole platform which provides different functionalities for a diverse hierarchy of medical professionals which supported by a state-of-the-art prediction model based on Deep Learning.
F1000Research
Automated human chromosome segmentation and feature extraction aim to improve the overall quality... more Automated human chromosome segmentation and feature extraction aim to improve the overall quality of genetic disorder diagnosis by addressing the limitations of tedious manual processes such as expertise dependence, time-inefficiency, observer variability and fatigue errors. Nevertheless, significant differences caused by staining methods, chromosome damage which may occur during imaging, cell and staining debris, inhomogeneity, weak boundaries, morphological variations, premature sister chromatid separation, as well as the presence of overlapping, touching, di-centric and bent chromosomes pose challenges in automated human chromosome segmentation and feature extraction. This review paper extensively discusses how the approaches presented in literature have addressed these challenges, and their strengths and limitations. Human chromosome segmentation algorithms are presented under four broad categories; thresholding, clustering, active contours and convex-concave points-based method...
Human Metaphase Chromosome Analysis using Image Processing (Thesis format: Monograph) by
Development of an effective human metaphase chromosome analysis algorithm can optimize expert tim... more Development of an effective human metaphase chromosome analysis algorithm can optimize expert time usage by increasing the efficiency of many clinical diagnosis processes. Although many methods exist in the literature, they are only applicable for limited morphological variations and are specific to the staining method used during cell preparation. They are also highly influenced by irregular chromosome boundaries as well as the presence of artifacts such as premature sister chromatid separation. Therefore an algorithm is proposed in this research which can operate with any morphological variation of the chromosome across images from multiple staining methods. The proposed algorithm is capable of calculating the segmentation outline, the centerline (which gives the chromosome length), partitioning of the telomere re-gions and the centromere location of a given chromosome. The algorithm also detects and corrects for the sister chromatid separation artifact in metaphase cell images. A...
Cytogenetic biodosimetry is the definitive test for assessing exposure to ionizing radiation. It ... more Cytogenetic biodosimetry is the definitive test for assessing exposure to ionizing radiation. It involves manual assessment of the frequency of dicentric chromosomes (DCs) on a microscope slide, which potentially contains hundreds of metaphase cells. We developed an algorithm that can automatically and accurately locate centromeres in DAPIstained metaphase chromosomes and that will detect DCs. In this algorithm, a set of 200-250 metaphase cell images are ranked and sorted. The 50 top-ranked images are used in the triage DC assay (DCA). To meet the requirement of DCA in a mass casualty event, we are accelerating our algorithm through parallelization. In this paper, we present our finding in accelerating our ranking and segmentation algorithms. Using data parallelization on a desktop system, the ranking module was up to 4-fold faster than the serial version and the Gradient Vector Flow module (GVF) used in our segmentation algorithm was up to 8-fold faster. Large scale data paralleliz...
Chromosome images used for "Centromere detection of human metaphase chromosome images using a candidate based method
Accurate detection of the human metaphase chromosome centromere is a critical element of cytogene... more Accurate detection of the human metaphase chromosome centromere is a critical element of cytogenetic diagnostic techniques, including chromosome enumeration, karyotyping and radiation biodosimetry. Existing centromere detection methods tends to perform poorly in the presence of irregular boundaries, shape variations and premature sister chromatid separation. We present a centromere detection algorithm that uses a novel contour partitioning technique to generate centromere candidates followed by a machine learning approach to select the best candidate that enhances the detection accuracy. The contour partitioning technique evaluates various combinations of salient points along the chromosome boundary using a novel feature set and is able to identify telomere regions as well as detect and correct for sister chromatid separation. This partitioning is used to generate a set of centromere candidates which are then evaluated based on a second set of proposed features. The proposed algorit...
Accurate detection of the human metaphase chromosome centromere is an critical element of cytogen... more Accurate detection of the human metaphase chromosome centromere is an critical element of cytogenetic diagnostic techniques, including chromosome enumeration, karyotyping and radiation biodosimetry. Existing image processing methods can perform poorly in the presence of irregular boundaries, shape variations and premature sister chromatid separation, which can adversely affect centromere localization. We present a centromere detection algorithm that uses a novel profile thickness measurement technique on irregular chromosome structures defined by contour partitioning. Our algorithm generates a set of centromere candidates which are then evaluated based on a set of features derived from images of chromosomes. Our method also partitions the chromosome contour to isolate its telomere regions and then detects and corrects for sister chromatid separation. When tested with a chromosome database consisting of 1400 chromosomes collected from 40 metaphase cell images, the candidate based cen...
Efficient Medical Video Streaming by Pre-Processing and Network Traffic Prioritization in Real-Time
Developing advanced healthcare applications to cater to the requirements of an ever-growing popul... more Developing advanced healthcare applications to cater to the requirements of an ever-growing population has become one of the key areas of research in engineering. One major application in this area is medical video streaming, which is often used for remote monitoring of patients. Medical video streaming helps to overcome geographical barriers and offers medical services at the convenience of the patient. However, as medical videos carry critical and time-sensitive information, retaining the quality and reducing latency during transmission is paramount for accurate medical diagnosis. This paper presents the concept of effective medical video streaming, which incorporates novel methods in video pre-processing, video compression, and transmission of medical data over optical networks.
Accurate detection of the human metaphase chromosome centromere is an critical element of cytogen... more Accurate detection of the human metaphase chromosome centromere is an critical element of cytogenetic diagnostic techniques, including chromosome enumeration, karyotyping and radiation biodosimetry. Existing image processing methods can perform poorly in the presence of irregular boundaries, shape variations and premature sister chromatid separation, which can adversely affect centromere localization. We present a centromere detection algorithm that uses a novel profile thickness measurement technique on irregular chromosome structures defined by contour partitioning. Our algorithm generates a set of centromere candidates which are then evaluated based on a set of features derived from images of chromosomes. Our method also partitions the chromosome contour to isolate its telomere regions and then detects and corrects for sister chromatid separation. When tested with a chromosome database consisting of 1400 chromosomes collected from 40 metaphase cell images, the candidate based cen...
Development of an effective human metaphase chromosome analysis algorithm can optimize expert tim... more Development of an effective human metaphase chromosome analysis algorithm can optimize expert time usage by increasing the efficiency of many clinical diagnosis processes. Although many methods exist in the literature, they are only applicable for limited morphological variations and are specific to the staining method used during cell preparation. They are also highly influenced by irregular chromosome boundaries as well as the presence of artifacts such as premature sister chromatid separation. Therefore an algorithm is proposed in this research which can operate with any morphological variation of the chromosome across images from multiple staining methods. The proposed algorithm is capable of calculating the segmentation outline, the centerline (which gives the chromosome length), partitioning of the telomere regions and the centromere location of a given chromosome. The algorithm also detects and corrects for the sister chromatid separation artifact in metaphase cell images. A measure termed the Candidate Based Centromere Confidence (CBCC) is proposed to accompany each centromere detection result of the proposed method, giving an indication of the confidence the algorithm has on a given localization. The proposed method was first tested for the ability of calculating an accurate width profile against a centerline based method [1] using 226 chromosomes. A statistical analysis of the centromere detection error values proved that the proposed method can accurately locate centromere locations with statistical significance. Furthermore, the proposed method performed more consistently across different staining methods in comparison to the centerline based approach. When tested with a larger data set of 1400 chromosomes collected from a set of DAPI (4',6-diamidino-2-phenylindole) and Giemsa stained cell images, the proposed candidate based centromere detection algorithm was able to accurately localize 1220 centromere locations yielding a detection accuracy of 87%. ii First and foremost, I would like to thank my main supervisor, Dr. Jagath Samarabandu for all the invaluable guidance and advice given to me throughout my PhD, both academically and personally. He has truly been a big influence and a role model for my academic career as well as for my personal life. I would like to thank my co-supervisor Dr. Peter Rogan as well as Dr. Joan Knoll for all their guidance and knowledge shared with me during my research. A great deal of appreciation needs to be shown to my course instructors, Dr. Ladak, Dr. Olga Veksler, Dr. Yuri Boykov and Dr. John Barron for their innovative and attractive ways of teaching and motivating my work. I would also like the thank Dr. Quazi Rahman for guiding me through my TA duties during this period.
Western Univ., London, ON, Canada
Orchestration of Advanced Motor Skills in a Group of Humans through an Elitist Visual Feedback Mechanism
2007 IEEE International Conference on System of Systems Engineering, 2007
Abstract A group of humans with diverse body dynamics and training backgrounds working on machine... more Abstract A group of humans with diverse body dynamics and training backgrounds working on machines with different dynamics can be considered as a system of live systems. This paper presents a method that can be adopted to automate the evolution of an elite skill in a factory of workers operating a given type of machines to produce a given product through successive induction of an evolved elite skill on other workers. It also proposed a simple model that can be used to explain complex phenomena that can not be explained by the ...
Orchestration of Advanced Motor Skills in a Group of Humans through an Elitist Visual Feedback Mechanism
A group of humans with diverse body dynamics and training backgrounds working on machines with di... more A group of humans with diverse body dynamics and training backgrounds working on machines with different dynamics can be considered as a system of live systems. This paper presents a method that can be adopted to automate the evolution of an elite skill in a factory of workers operating a given type of machines to produce a given product through successive induction of an evolved elite skill on other workers.
Automatic Detection of Pale Path and Overlaps in Chromosome Images using Adaptive Search Technique and Re-thresholding
Intensity integrated Laplacian algorithm for human metaphase chromosome centromere detection
2012 25th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), 2012
ABSTRACT Centromere localization in human metaphase chromosomes is an essential task in many cyto... more ABSTRACT Centromere localization in human metaphase chromosomes is an essential task in many cytogenetic diagnosis procedures. The centromere location can be utilized to derive information such as the chromosome type, polarity assignment etc. Methods available in literature yield unreliable results mainly due to high variability of morphology in metaphase chromosomes and boundary noise in the image. In this paper we have proposed a multi-staged algorithm which utilizes both contour information as well as intensity information to obtain a more accurate centromere location. The width information along the axis of symmetry is obtained using a novel Laplacian based thickness measurement algorithm. The proposed method was observed to be more accurate compared to the state of the art when tested with 226 human metaphase chromosomes.
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Papers by Akila Subasinghe