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Proceedings of 2019 9Th International Conference on Bioscience, Biochemistry and Bioinformatics (ICBBB 2019) Preface……………………..………………………………………………………….… Proceedings of 2019 9th International Conference on Bioscience, Biochemistry and Bioinformatics ICBBB 2019 Singapore January 7-9, 2019 ISBN: 978-1-4503-6654-0 The Association for Computing Machinery 2 Penn Plaza, Suite 701 New York New York 10121-0701 ACM ISBN: 978-1-4503-6654-0 ACM COPYRIGHT NOTICE. Copyright © 2019 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept., ACM, Inc., fax +1 (212) 869-0481, or [email protected]. For other copying of articles that carry a code at the bottom of the first or last page, copying is permitted provided that the per-copy fee indicated in the code is paid through the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923, +1-978-750-8400, +1-978-750-4470 (fax). Table of Contents Proceedings of 2019 9th International Conference on Bioscience, Biochemistry and Bioinformatics (ICBBB 2019) Preface……………………..………………………………………………………….…. ……..……………… V Conference Committees…………………………………………………………………………………...…..VI Session 1- Biomedical Imaging and Image Processing An Unsupervised Learning with Feature Approach for Brain Tumor Segmentation Using Magnetic 1 Resonance Imaging Khurram Ejaz, Mohd Shafy Mohd Rahim, Usama Ijaz Bajwa, Nadim Rana and Amjad Rehman Bessel-Gauss Beam Light Sheet Assisted Fluorescence Imaging of Trabecular Meshwork in the Iridocorneal 8 Region Using Long Working Distance Objectives C. S. Suchand Sandeep, Sarangapani Sreelatha, Mani Baskaran, Xun Jie Jeesmond Hong, Tin Aung and Vadakke Matham Murukeshan Semantic Segmentation of Colon Gland with Conditional Generative Adversarial Network 12 Liye Mei, Xiaopeng Guo and Chaowei Cheng Image Processing, Textural Feature Extraction and Transfer Learning based detection of Diabetic 17 Retinopathy Anjana Umapathy, Anusha Sreenivasan, Divya S. Nairy, S Natarajan and B Narasinga Rao Session 2- Bioinformatics and Biosignal Analysis Establishment of an Integrated Computational Workflow for Single Cell RNA-Seq Dataset 22 Miaomiao Jiang, Qichao Yu, Jianming Xie and Shiping Liu An Interactive Gameplay to Crowdsource Multiple Sequence Alignment of Genome Sequences: Genenigma 28 D. A. Meedeniya, S. A. P. A. Rukshan and A. Welivita A Hybrid Approach to Optimize the Number of Recombinations in Ancestral Recombination Graphs 36 Nguyen Thi Phuong Thao and Le Sy Vinh CNN-SVR for CRISPR-Cpf1 Guide RNA Activity Prediction with Data Augmentation 43 Guishan Zhang and Xianhua Dai Extraction of Respiration from PPG Signals Using Hilbert Vibration Decomposition 48 Hemant Sharma Session 3- Biological Systems Modeling and Simulation Calcium Signaling and Finite Element Technique 53 Devanshi D. Dave and Brajesh Kumar Jha III A Fractional Mathematical Model to Study the Effect of Buffer on Calcium Distribution in Parkinson’s 58 Disease Hardik Joshi and Brajesh Kumar Jha Computational Modelling of Calcium Buffering in a Star Shaped Astrocyte 63 Amrita Jha and Brajesh Kumar Jha Stability of Mitotic Spindle Using Computational Mechanics 67 Andrii Iakovliev, Srinandan Dasmahapatra and Atul Bhaskar Session 4- Bioscience and Biotechnology Research on Response Surface Optimization of Culture Medium for Antibacterial Substances Produced by 75 Bacillus Amyloliquefaciens GN59 Liu Yaping, Zhao Bo, Emiliya Kalamiyets, Wu Peng and Chu Jie Effected Brewing Time and Temperature of Centella Asiatica Tea on Antioxidant Activity and Consumer 82 Acceptance Rungnattakan Ploenkutham, Preeyapa Sripromma, Suksan Amornraksa, Patchanee Yasurin, Malinee Sriariyanun, Suvaluk Asavasanti and Aussama Soontrunnarudrungsri β-Cyclodextrin Inclusion Complex with Dimethyl[4-hydroxypiperidin-4-yl]Phosphonates as Green Plant 86 Growth Stimulators A. Ye. Malmakova, V. K. Yu, N. U. Kystaubayeva, A. G. Zazybin and T. E. Li Antimicrobial Activity of Lichens-Associated Actinomycetes Strain LC-23 91 Agustina E. Susanti, Shanti Ratnakomala, W. Mangunwardoyo and Puspita Lisdiyanti Comparative Studies Between Hydrothermal Carbonation and Torrefaction for Biofuel Production from 97 Poultry Litter Rafail Isemin, Aleksandr Mikhalev, Oleg Milovanov, Dmitry Klimov, Natalia Muratova, Kristina Krysanova, Yuri Teplitskii, Anatolii Greben’kov and Vadim Kogh-Tatarenko Session 5- Statistical Ecology Ecological Informatics Approach to Analyze Habitat Preferences of Auricularia delicata in Bingungan 102 Forest, Turgo Natural Forest Conservation Area Dwiki Prasetiya and Tien Aminatun 109 Author Index IV ICBBB 2019 Biomedical Imaging and Image Processing An Unsupervised Learning with Feature Approach for Brain Tumor Segmentation Using Magnetic Resonance Imaging Khurram Ejaz Mohd Shafy Mohd Rahim Usama Ijaz Bajwa Faculty of Computing, UTM Faculty of Computing, UTM Computer Science department, Johor Bahru Johor Bahru COMSATS University Islamabad Malaysia Malaysia Lahore Campus, Pakistan [email protected] [email protected] [email protected] Nadim Rana Amjad Rehman Faculty of CCIS, Jazzan University Faculty of CCIS Jazzan, Saudi Arabia Al Yamama University, Riyadh, Saudi Arabia [email protected] [email protected] ABSTRACT features, statistical features, Kmean, FCM. Dice index (DOI), Segmentation methods are so much efficient to segment complex Jaccard Index(JI) tumor from challenging datasets. MACCAI BRATS 2013-2017 brain tumor dataset (FLAIR, T2) had been taken for high grade 1. INTRODUCTION glioma (HGG). This data set is challenging to segment tumor due Images are composing of voxel of pixels, it has been seen in field to homogenous intensity and difficult to separate tumor boundary of image processing. Segmentation is one of performance-oriented technique to segment within boundary or detect edge in image and from other normal tissues, so our goal is to segment tumor from mixed intensities. It can be accomplished step by step. Therefore its application also exhibits in medical imaging like MRI, CT scan image maximum and minimum intensities has been adjusted to segment region of interest. because need to highlight the tumor portion then thresholding Magnetic Resonance Imaging (MRI) gives important information perform to localize the tumor region, has applied statistical inside human body like brain and other organ which are scanned features(kurtosis, skewness, mean and variance) so tumor portion by doctor using MRI machine, abnormalities are investigated for become more visualize but cann’t separate tumor from boundary treatment. These machines have scanner and capture image and then apply unsupervised clusters like kmean but it gives hard intensities, normally they get variation therefore MRI resultant crisp membership and many tumor membership missed so texture image possess intensity biasness and machine creates image features(Correlation, energy, homogeneity and contrast) with which consists of more homogenous pattern and organ become combination of Gabor filter has been applied but dimension of hide, therefore we need to handle intensity distribution to see our data increase and intensities became disturb due high dimension region of interest. operation over MRI. Tumor boundary become more visualize if combine FLAIR over T2 sequence image then we apply FCM and MRI sequences are T1, T1CE, T2, FLAIR (Fluid attenuated result is: tumor boundaries become more visualized then applied inversion recovery). T1 weighted depict white matter (as inner) one statistical feature (Kurtosis) and one texture feature(Energy) and grey matter (as outer) tissues region whereas t2 weighted so tumor portion separate from other tissue and better images are more contrast or darker image and their boundaries are segmentation accuracy have been checked with comparison clearer with darkness. T2 and FLAIR images have been parameters like dice overlap and Jaccard index. segmented in this paper. Features plays important role for identification of variability CCS Concepts among different shapes, they work with combination of un Applied computing → Life and medical sciences → supervised clustering techniques to give better segmentation Computational biology → Imaging results for detection of brain tumor. Keywords The organization of this paper is as, section number 2 is consisting SOM-FKM; Magnetic Resonance Imaging (MRI); Texture of related work which give very clear picture, section number 3 is pertaining material which have taken from BRATS MACCAI Permission to make digital or hard copies of all or part of this work for 2017-2013 brain tumor dataset. Section number four reflects personal or classroom use is granted without fee provided that copies methodology of our work, section number 5 ‘RESULT AND are not made or distributed for profit or commercial advantage and that Discussion’s analysis portion of this work and last portion is copies bear this notice and the full citation on the first page. conclusion and future work. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, 2. RELATED WORK requires prior specific
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