Journal of image processing & pattern recognition progress (vol1, issue1) by STM Journals Publication

Journal of Image Processing &

Pattern Recognition Progress

Jan - April 2014 (JoIPPRP)

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Journal of Image Processing & Pattern Recognition Progress Focus and Scope Covers † Image digital representation † Biometrics † New algorithms and/or technologies for biometrics † Element of visual perception † Analysis of specific applications †

Restoration Models: Constrained & Unconstrained

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Editorial Board

Chun Ming Chang Assistant Professor Department of Applied Informatics and Multimedia Asia University Wufeng, Taichung 41354, Taiwan.

Prof. Hsu-Yung Cheng Associate Professor Department of Computer Science and Information Engineering National Central University, Taiwan.

Dr. Abhishek Das Assistant Professor Dept. of Information Technology, Tripura University (A Central University) Suryamaninagar, Agratala.

Dr. Rameswar Debnath Head Computer Science and Engineering Discipline. Khulna University, Khulna 9208, Bangladesh.

Dr. V. K. Govindan Professor Computer science and engineering, and Dean Academic National Institute of Technology Calicut, Kerala, India.

Dr. Bijan Karimi Professor Electrical & Computer Engineering and Computer Science Tagliatela College of Engineering, India.

Dr. Hari Om Assistant Professor Department of Computer Science & Engineering Indian School of Mines Dhanbad826 004 Jharkhand India.

Arup Kumar Pal Assistant Professor Department of Computer Science and Engineering Indian School of Mines, Dhanbad Jharkhand-826004, India.

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Gonzalo Vegas Associate Teacher University of Valladolid (Spain).

Director's Desk

STM JOURNALS

I take the privilege to present the print version for the [Volume 1 Issue (1)] of Journal of Image Processing & Pattern Recognition Progress. The intension of JoIPPRP is to create an atmosphere that stimulates creativeness, research and growth in the area of image processing. The development and growth of the mankind is the consequence of brilliant Research done by eminent Scientists and Engineers in every field. JoIPPRP provides an outlet for Research findings and reviews in areas of image processing found to be relevant for National and International recent developments & research initiative. The aim and scope of the Journal is to provide an academic medium and an important reference for the advancement and dissemination of Research results that support high level learning, teaching and research in the domain of image processing. Finally, I express my sincere gratitude and thanks to our Editorial/ Reviewer board and Authors for their continued support and invaluable contributions and suggestions in the form of authoring write ups/ reviewing and providing constructive comments for the advancement of the journals. With regards to their due continuous support and co-operation, we have been able to publish quality Research/Reviews findings for our customers base. I hope you will enjoy reading this issue and we welcome your feedback on any aspect of the Journal.

Dr. Archana Mehrotra Director STM Journals

Journal of Image Processing & Pattern Recognition Progress

Contents

1. Estimation of Convolution Masks for MRI Image Restoration using Genetic Algorithm Ashwani Kumar, Yogesh Kumar

2. Performance Comparison between Back-Propagation Learning and Kohonen Self-Organizing Neural Networks Algorithm in Terms of Pattern Recognition Md. Rabiul Islam

3. Region Segmentation and Annotation with Vehicle Detection Validation Application in Airborne Images Hsu-Yung Cheng, Ding-Wen Wu

4. Image Segmentation based on Region Merging using Breadth-First Search Amandeep Kaur, Neeru Jindal

5. Q-Metrics for Early Detection of Cervical Cancer Das A., Kar A., Bhattacharyya D.

Journal of Image Processing & Pattern Recognition Progress Volume 1, Issue 1 www.stmjournals.com

Estimation of Convolution Masks for MRI Image Restoration using Genetic Algorithm Ashwani Kumar1*, Yogesh Kumar2 1

Neelkanth Institute of Technology Meerut, UP, India Translam Institute of Technology Meerut, UP, India

Abstract The present paper proposes the technique for the restoration of images using convolution masks generated using GA. Image restoration is carried out to recover a corrected image from a degraded image and is specific to the type of degradation. In image restoration there is need to build the specific mathematical model for degradation or degradation function hence there is need to know about the cause of degradation without which some time it becomes impossible to correct the image. In most practical cases there is not enough information available about the degradation, and is needed to be estimated either analytically or empirically. The level of problem of image restoration is further increased by the presence of noise, and more than one cause of degradation, as in these circumstances it becomes difficult to formulate the mathematical model or degradation function. To alleviate this difficulty we have proposed method to generate convolution mask using GA for application in image restoration technique. The proposed algorithm has been tested on images simulated with the motion blurring and for the presence of noise. Finally the algorithm is applied to correct the motion artefact in Computed tomography images; the results obtained are promising and can be applied to other area of imaging also.

Keywords: convolution masks, image restoration, genetic algorithm.

Journal of Image Processing & Pattern Recognition Progress Volume 1, Issue 1 www.stmjournals.com

Performance Comparison between Back-Propagation Learning and Kohonen Self-Organizing Neural Networks Algorithm in Terms of Pattern Recognition Md. Rabiul Islam* Department of Computer Science & Engineering, Rajshahi University of Engineering & Technology Rajshahi, Bangladesh

Abstract Pattern recognition using back-propagation learning and Kohonen self-organizing neural network algorithms has been developed and measured various performance based on different criteria and environment of the pattern. These pattern recognition systems have taken the object image as input. In image pre-processing stage, scaling and clipping process has been applied from the background image to avoid unnecessary portion of the object image. Feature extraction has been performed after applying filtering and edgedetection method. The extracted feature has been used as the input of the backpropagation learning neural networks (BPN) algorithm and Kohonen self-organizing mapping (SOM) algorithm. Networks have been trained to create the knowledgebase from the input features. Finally, these learned templates have been used for testing purpose. The difference between two training procedure is that the learned weights and thresholds have been updated to calculate the output for BPN and feature mapping technique in output grid has been used for Kohonen network. Finally, the performances of both algorithms have been measured and compared the learning and recognition performance on the various selected criteria.

Keywords: Back-propagation learning neural networks, kohonen self-organizing mapping neural network, feture extraction, pattern recognition

Journal of Image Processing & Pattern Recognition Progress Volume 1, Issue 1 www.stmjournals.com

Region Segmentation and Annotation with Vehicle Detection Validation Application in Airborne Images Hsu-Yung Cheng*, Ding-Wen Wu Department of Computer Science and Information Engineering, National Central University, Taiwan

Abstract In this work, the authors propose an automatic image segmentation and annotation system for airborne images. Initial region segmentation using existing region segmentation methods is applied to airborne images first. To deal with over-segmentation on the initial region segmentation results, the authors performed graph-based region merging by constructing an undirected-graph based on 8-connected local neighborhood. For each region, the authors extracted low-level features and used the Support Vector Machine (SVM) classifier to annotate the region with labels. Based on the output of the SVM classifier, adjacent regions with the same label were further merged to obtain the final segmentation and labeling result. The segmentation and annotation results are useful for vehicle detection validation. The experiments show that the proposed system can effectively segment and label various aerial images on a highly challenging dataset. Also, vehicle detection can be substantially enhanced with the help of the proposed annotation results and validation scheme.

Keywords: Airborne images, vehicle detection, region segmentation, annotation

Journal of Image Processing & Pattern Recognition Progress Volume 1, Issue 1 www.stmjournals.com

Image Segmentation based on Region Merging using Breadth-First Search Amandeep Kaur*, Neeru Jindal Electronics & Communication, R.I.E.I.T Railmajra, SBS Nagar, India Abstract This paper proposed a new method for image segmentation based on region merging using breadth-first search (BFS). The image can be partitioned into multiple segments so that meaningful information is extracted out and then image is analyzed easily. In the proposed method, first the oversegmented image is obtained by applying a standard watershed transformation on original image. Then BFS is executed on the oversegmented image to obtain a segmented image. The quality parameter F-measure has been calculated for the segmented images. The proposed algorithm is also compared with existing method and better results are obtained.

Keywords: Image segmentation, watershed transformation, BFS.

Journal of Image Processing & Pattern Recognition Progress Volume 1, Issue 1 www.stmjournals.com

Q-Metrics for Early Detection of Cervical Cancer Das A.1*, Kar A.2, Bhattacharyya D.3 1

Department of Information Technology, Tripura University (A Central University), Agartala, Tripura, India 2 Department of Computer Science & Engineering, Jadavpur University, Kolkata, West Bengal, India 3 Department of Gynecology & Obstetrics, College of Medicine & SD Hospital, Kolkata, West Bengal, India

Abstract The most prevalent form of cancer in women worldwide is uterine cervical cancer. Through screening programs aimed at detecting precancerous lesions most cases of cervical cancer can be prevented. In this article, Q-metrics has been proposed for carrying out automated image segmentation of uterine cervical cancer. The validation of detection of cervical lesions is an important issue in medical image processing because it has a specific impact on surgical planning. We evaluated the segmentation accuracy on the basis of a four-sample validation metric against the estimated gold standard, which was derived from several domain expertsâ&#x20AC;&#x2122; manual segmentations by a novel algorithm. The distribution functions of the lesion and control pixel data were parametrically assumed to be a mixture of two beta distributions with different shape parameters. We estimated the corresponding receiver operating characteristic (R.O.C.) curve and Dice similarity coefficient (D.S.C.) in all possible decision thresholds. The automated segmentation yielded satisfactory accuracy with varied optimal thresholds.

Keywords: cervical cancer, automated segmentation, E.M. algorithm, sensitivity, specificity, clustering, R.O.C., D.S.C.