Sistem saringan penyakit pterigium untuk imej mata terangkum hadapan

Pterigium merupakan penyakit berkaitan mata yang melibatkan penumbuhan tisu menyelaputi kawasan kornea mata. Hal ini kebiasaannya menjejaskan individu yang menetap di kawasan Khatulistiwa seperti Malaysia dan terdedah kepada keadaan angin, radiasi ultraviolet atau debu yang berlebihan. Salah satu cara pengesanan pterigium secara konvensional adalah melalui kaedah saringan manual oleh pakar oftalmologi. Pterigium didiagnos selepas pemeriksaan fizikal mata dilakukan. Gambar mata diambil bagi tujuan memantau pertumbuhan tisu pterigium. Sekiranya perlu, ujian diagnostik khusus akan dilakukan terutamanya apabila pterigium menyelaputi kawasan kornea mata. Contohnya, topografi kornea akan digunakan untuk menandakan permukaan kornea bagi mengesan sebarang gangguan yang mungkin timbul seperti penumbuhan tisu pterigium yang semakin membesar. Pada pengetahuan kami, hanya sedikit sahaja penyelidikan yang melibatkan kaedah pemprosesan imej digital (PID) untuk mengesan penyakit berkaitan mata pada peringkat awal menggunakan imej mata terangkum hadapan (IMTH). Oleh itu, projek ini mencadangkan algoritma untuk mengesan penyakit pterigium menggunakan IMTH yang didapati daripada empat pangkalan data yang berbeza iaitu UBIRIS, MILES, RAFAEL dan QPEI. Sistem saringan yang dicadangkan terdiri daripada empat modul utama iaitu modul-modul pengumpulan data IMTH, peruasan kornea, penyarian fitur dan pengesanan pterigium. Melalui pengiraan nisbah jejari pada kawasan peruasan kornea menggunakan nilai ambang 1.00, keputusan pengesanan pterigium yang diperolehi adalah 90.60% Positif Benar (PB), 77.24% Negatif Benar (NB), 22.76% Positif Palsu (PP) dan 9.40% Negatif Palsu (NP)

Penuras terbitan Gaussian berorientasi untuk peruasan imej paru-paru radiograf mesin pegun dan mudah alih

Kaedah peruasan paru-paru tanpa seliaan adalah proses mandatori bagi membangunkan Sistem Dapatan Semula Imej Perubatan Berdasarkan Kandungan (CBMIRS) untuk imej sinar-x dada (CXR). Setakat ini, kajian berkenaan CXR bagi mesin mudah alih sangat terhad walhal ianya penting kerana pesakit yang tenat akan didiagnos menggunakan mesin mudah alih. Kajian ini membentangkan penyelesaian yang kukuh untuk peruasan paru-paru CXR bagi mesin pegun dan mudah alih, dengan kaedah automatik berasaskan penuras terbitan Gaussian dengan tujuh orientasi, digabungkan dengan teknik pengklusteran Fuzzy C-Means dan pengambangan untuk memperincikan kerangka paru-paru. Algoritma baru untuk menghasilkan nilai ambang secara automatik bagi setiap tindak balas Gaussian juga diperkenalkan. Algoritma ini digunakan untuk kedua-dua CXR PA dan AP daripada set data awam (JSRT) dan persendirian yang diperolehi daripada hospital kolaboratif. Dua blok pra-pemprosesan diperkenalkan untuk menyeragamkan imej dari mesin yang berbeza. Perbandingan dengan kajian terdahulu yang menggunakan set data JSRT menunjukkan kaedah kami menghasilkan keputusan yang memberangsangkan. Penilaian prestasi (ketepatan, F-skor, kepersisan, kepekaan dan kekhususan) bagi peruasan dari set data JSRT adalah lebih daripada 0.90, kecuali skor-bertindih (0.87). Nilai median skor-bertindih bagipangkalan data imej persendirian adalah 0.83 (mesin pegun) dan 0.75 (dari dua jenis mesin mudah alih). Algoritma ini juga pantas, dengan purata masa pelaksanaan 12.5s. Kaedah ini berupaya beroperasi tanpa penyeliaan, latihan atau pembelajaran untuk peruasan paru-paru bagi radiograf yang diambil dari mesin yang mempunyai piawaian berbeza, serta berupaya untuk digunakan dalam aplikasi CBMIRS

Meeting the Needs of Fourth Industrial Revolution (4IR) in Entrepreneurial Education in Malaysia: The Government’s Role

Entrepreneurship education holds great value for all students of science, technology, mission work, social work, healthcare, and education. It also serves as a great incubator for the types of creative, innovative ideas of our students and the global needs in the 21st century where combining entrepreneurship syllabus and exposure of the fourth industrial revolution is essential. This study explores the Fourth Industrial Revolution (4IR) as an opportunity to change models of innovation-driven entrepreneurship for the better, and create an environment that makes entrepreneurship more inclusive, while maximizing the Fourth Industrial Revolution’s benefits to the society and minimizing the risks that come with it. The role of Malaysian government in enhancing entrepreneurial education must therefore recognize the fourth industrial evolution and its impacts that must be compatible with Malaysia’s industry policy. Promotion of entrepreneurial experimentation within an appropriate entrepreneurial education ecosystem will provide entrepreneurs with smart government support that invests in entrepreneurial skills in Malaysia. This article assesses (i) fourth industrial revolution impact on entrepreneurial education; (ii) new expectations arising from impacts of fourth industrial evolution in Malaysia: method in teaching and learning; (iii) government’s role in supporting entrepreneurship education and finally (iv) entrepreneurial education reforms in Malaysia

Future stem cell analysis: progress and challenges towards state-of-the art approaches in automated cells analysis

Background and Aims A microscopic image has been used in cell analysis for cell type identification and classification, cell counting and cell size measurement. Most previous research works are tedious, including detailed understanding and time-consuming. The scientists and researchers are seeking modern and automatic cell analysis approaches in line with the current in-demand technology. Objectives This article provides a brief overview of a general cell and specific stem cell analysis approaches from the history of cell discovery up to the state-of-the-art approaches. Methodology A content description of the literature study has been surveyed from specific manuscript databases using three review methods: manuscript identification, screening, and inclusion. This review methodology is based on Prism guidelines in searching for originality and novelty in studies concerning cell analysis. Results By analysing generic cell and specific stem cell analysis approaches, current technology offers tremendous potential in assisting medical experts in performing cell analysis using a method that is less laborious, cost-effective, and reduces error rates. Conclusion This review uncovers potential research gaps concerning generic cell and specific stem cell analysis. Thus, it could be a reference for developing automated cells analysis approaches using current technology such as artificial intelligence and deep learning

Sistem dapatan semula imej untuk aplikasi perubatan

Dapatan semula imej (DSI) adalah sistem pencarian imej yang menggunakan ciri-ciri tertentu atau konteks khusus dalam sesuatu imej. Dalam bidang perubatan, sistem DSI digunakan untuk menyediakan imej yang diperlukan secara tepat dan pantas kepada pakar perubatan. Proses itu biasanya berlaku pada dan ketika diagnosis dan rawatan penyakit dilakukan. Sistem dapatan semula yang awal dan masih digunakan dengan meluas dalam bidang perubatan adalah sistem DSI berdasarkan teks (TBIRS). TBIRS menggunakan kata kunci dalam konteks sesuatu imej dan ia memerlukan anotasi teks secara manual. Proses anotasi teks adalah tugas yang memerihkan lebih-lebih lagi jika melibatkan pangkalan data yang besar. Ini memungkinkan kebarangkalian berlakunya kesilapan manusia adalah tinggi. Untuk mengatasi masalah yang dinyatakan, sistem DSI berdasarkan kandungan (CBIRS) dengan pengindeksan automatik adalah dicadangkan. Kaedah ini melibatkan pemprosesan imej perubatan berdasarkan komputer yang menggunakan fitur visual imej seperti warna, bentuk dan tesktur. Namun begitu, umum mengetahui bahawa suatu algoritma tertentu dalam CBIRS adalah khusus untuk satu modaliti sahaja dan melibatkan bahagian yang tertentu. Ini ditambahkan pula bahawa CBIRS telah mengabaikan persepsi manusia dalam tugas menakrif sesuatu imej dan akibatnya, menyebabkan wujudnya masalah jurang semantik. Oleh itu, sistem DSI hibrid (HBIRS) yang menggabungkan kekuatan kedua-dua TBIRS dan CBIRS telah diperkenalkan bagi menangani masalah jurang semantik khususnya dan sekaligus memantapkan sistem DSI amnya. Satu kerangka sistem DSI yang cekap iaitu HBIRS juga telah dicadangkan. Walau bagaimanapun, kajian ini hanya melibatkan TBIRS dan CBIRS bagi aplikasi perubatan, dan prototaip TBIRS yang dikaji menggunakan imej X-Ray turut dicadangkan

Deep learning for an automated image-based stem cell classification

Hematopoiesis is a process in which hematopoietic stem cells produce other mature blood cells in the bone marrow through cell proliferation and differentiation. The hematopoietic cells are cultured on a petri dish to form a different colony-forming unit (CFU). The idea is to identify the type of CFU produced by the stem cell. Several software has been developed to classify the CFU automatically. However, an automated identification or classification of CFU types has become the main challenge. Most of the current software has common drawbacks, such as the expensive operating cost and complex machines. The purpose of this study is to investigate several selected convolutional neural network (CNN) pre-trained models to overcome these constraints for automated CFU classification. Prior to CFU classification, the images are acquired from mouse stem cells and categorized into three types which are CFU-erythroid (E), CFU-granulocyte/macrophage (GM) and CFU-PreB. These images are then pre-processed before being fed into CNN pre-trained models. The models adopt a deep learning neural network approach to extract informative features from the CFU images Classification performance shows that the models integrated with the pre-processing module can classify the CFUs with high accuracies and shorter computational time with 96.33% on 61 minutes and 37 seconds, respectively. Hence, this work finding could be used as the baseline reference for further research.Keywords: Automated stem cell classification; Colony-forming unit (CFU); Deep learning; Convolutional neural network (CNN) Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, MalaysiabOptometry and Vision Sciences Programme, Faculty of Health Sciences, School of Healthcare Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia*proliferation and differentiation. The hematopoietic cells are cultured on a petri dish to form a different colony-forming unit (CFU). The idea is to identify the type of CFU produced by the stem cell. Several software has been developed to classify the CFU automatically. However, an automated identification or classification of CFU types has become the main challenge. Most of the current software has common drawbacks, such as the expensive operating cost and complex machines. The purpose of this study is to investigate several selected convolutional neural network (CNN) pre-trained models to overcome these constraints for automated CFU classification. Prior to CFU classification, the images are acquired from mouse stem cells and categorized into three types which are CFU-erythroid (E), CFU-granulocyte/macrophage (GM) and CFU-PreB. These images are then pre-processed before being fed into CNN pre-trained models. The models adopt a deep learning neural network approach to extract informative features from the CFU images Classification performance shows that the models integrated with the pre-processing module can classify the CFUs with high accuracies and shorter computational time with 96.33% on 61 minutes and 37 seconds, respectively. Hence, this work finding could be used as the baseline reference for further research

Methods for Clustered Microcalcifications Detection in Digital Mammograms

This theis presents the comparison of three automated methods for an early detection of breast cancer that used to detect clusters of microcalcifications (MCCs), which are associated with a high probability of malignancy. The proposed methods are based on several image processing concepts, such as morphological approach, fractal analysis, adaptive wavelet transform, local maxima detection and high -order statistics (HOS) tests

Content-based medical image analysis and retrieval of intracranial haemorrhages CT brain images

Over the last few decades, medical image retrieval has become one of the most exciting and fastest growing research areas in the application of image retrieval due to the need for computer-assisted classification, query, and retrieval methods for large medical image archives. These automation processes offset the high cost of manual annotation by medical experts, which is cumbersome, prone to errors, and prohibitively expensive as well as dependent on human subjectivity. One solution to such problems is to fully automate the segmentation and feature extraction processes for the development of content-based medical image retrieval (CBMIR). The existing approaches strongly focus on a particular imaging modality with the queries restricted to a well-defined diagnostic background. Hence, the main motivation of this thesis is to develop a fully automated segmentation approach together with a reliable feature extraction method to be used in a CBMIR system for intracranial haemorrhage (ICH) in Computed Tomography (CT) brain images. To overcome the volume partial effect and inconsistency of grey-level values of the CT brain images, multi-level thresholding methods are proposed. These level by-level segmentation approaches are fully automated and able to extract the intracranial and skull information from the CT brain images. The intracranial is subsequently further segmented into cerebrospinal fluid, brain tissues, and other homogenous regions useful for detecting any abnormalities (i.e., bleeding, calcification, misaligned ventricles) that may be present in the brain. In addition, the extracted skull is useful to represent a skull feature vector for skull fracture detection. The proposed approach promotes more effective segmentation compared to other fully automated methods discussed in previous literature. To develop a reliable and efficient CBMIR, namely for ICH, a Binary Coherent Vector (BCV) approach, which is part of the feature extraction process, is proposed. This work demonstrates that the combination of geometric shapes and Hu moment invariant features provides the best feature vectors for distinguishing haemorrhage shape, resulting in an average precision rate of up to 80% during the first 60% of the average recall using both normalized Manhattan and normalized Euclidean as well as Mahalanobis distance metrics. These results are promising and provide a strong basis for the application of CBMIR, specifically for CT brain images

Segmenting Retinal Blood Vessels with Gabor Filter and Automatic Binarization

For timely diagnosis of retinal disease, routine retinal monitoring of people with high risk should be put in place. To assist the ophthalmologists in performing retinal analysis efficiently and accurately, numerous studies have been conducted to propose an automated retinal diagnosis system. One of the crucial steps for such a system is accurate detection of retinal blood vessels from retinal image. In this paper, we investigated the use of automatic binarization methods on pre-processed fundus image to detect retinal blood vessels. Three methods for binarization were investigated in this study, namely Otsu’s method, ISODATA and K-means clustering method. The resulting binarized output indicated good detection of large vessels but most of the smaller vessels were left undetected. To address this issue, Gabor wavelet filter was used to enhance the small blood vessel structures before binarization of the filter output. Combining the binary images from both binarization with and without Gabor filter resulted in significant improvement of the overall detection rate of the retinal blood vessels. The proposed method proved to be comparable to other unsupervised techniques in the literature when validated using the publicly available fundus image database, DRIVE