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i-manager's Journal on Image Processing (JIP)

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Identification of Volcano Hotspots by using Resilient Back Propagation (RBP) Algorithm Via Satellite Images
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Contrast Enhancement based Brain Tumour MRI Image Segmentation and Detection with Low Power Consumption
Denoising of Images by Wavelets and Contourlets using Bi-Shrink Filter

Volume 7 Issue 1 January - March 2020

Research Paper

An Improved Hardware-Oriented Level Set (Hols) for Complex Image Segmentation

Souleymane Balla-Arabe*

State key Laboratory of Integrated Services Networks, Xi’an, China.

Balla-Arabé, S. (2020). An Improved Hardware-Oriented Level Set (HOLS) for Complex Image Segmentation. i-manager's Journal on Image Processing , 7(1), 1-14. https://doi.org/10.26634/jip.7.1.17087

Abstract

Level Set Methods (LSMs) offer many advantages comparing with traditional snake models, among others, it has the ability to automatically handle topological changes. Nevertheless, they are computationally expensive. Today's solution to this problem is the implementation using massively parallel devices such as Graphics Processing Unit (GPU), while the development of the AMD's Heterogeneous System Architecture (HSA) systems will definitively provide future effective solutions. However, for the LSM to be suitable to parallel architectures, the curve evolution should be local and almost all the LSMs use regional statistics in order to be effective. Those whouse fully local statistics suffer from a lack of accuracy in detecting boundaries without well-defined edges. In this work, we design a novel hardware-oriented LSM, adequate to future HSA systems and today's hybrid platforms. The non-local step is done by the CPU, while the fully local curve evolution is executed in the GPU. Compared with state-of-the-art methods, this method presents better results both in terms of effectiveness and efficiency. Furthermore, it allows the detection of multiclass boundaries by using only one Level Set Function (LSF) whatever the number of classes n , while the most effective multiphase LSMs would use log n LSF. 2 Intensive experiments demonstrate the high performance of the proposed framework.

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Research Paper

Achieving Image Compression in Medical Imaging using Baseline Approach

Jimoh Abdul Ganiyu* , Ajayi Olusola Olajide, Orimoloye Segun Michael *, Aju Omojokun Gabrielis****

* - **** Department of Computer Science, Adekunle Ajasin University, Akungba–Akoko, Ondo, Nigeria.

Ganiyu, J. A., Olajide, A. O., Michael, O. S., and Gabrielis, A. O. (2020). Achieving Image Compression in Medical Imaging using Baseline Approach. i-manager's Journal on Image Processing , 7(1), 15-23. https://doi.org/10.26634/jip.7.1.16918

Abstract

Telemedicine aims at providing a reliable and quality health care for continuous diagnosis and to provide patients care and treatment at an affordable cost. Compression is a process used to reduce the physical size of the information, resulting in minimum space usage. The time taken for transmission and bandwidth on the network are reduced. Compression of medical images is an area and process that ensures the dividend of telemedicine. This study takes in- depth look into the development and enhancement of the work done by other authors, and therefore focuses on a baseline image for compressing medical images. The study adopted a digital image processing approach with appropriate encoding algorithm, designated one of these images as a baseline image, computes the difference between the baseline image and the other images, and then lossless compress them. The results of the findings show that lossless compression technique with compression rate greater than 4.0, provides better compression accuracy compared to a compression rate less than 4.0.

References

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Research Paper

Hybrid Equation Resulting from the Combination of a Second-Order Equation with a Fourth-Order Equation for the Restoration of Noisy Images

Simo Thierry* , Ntsama Eloundou Pascal, Noura Alexendre*

* - *** Department of Physics, University of Ngaoundere, Ngaoundere, Cameroon.

Thierry, S., Pascal, N. E., and Alexendre, N. (2020). Hybrid Equation Resulting from the Combination of a Second-Order Equation with a Fourth-Order Equation for the Restoration Noisy Images. i-manager's Journal on Image Processing , 7(1), 24-34. https://doi.org/10.26634/jip.7.1.17038

Abstract

The filtering of images is an important task in the research area of image processing. The diffusion equations are frequently used in image processing to solve the problem of staircase effect introduced by the second order diffusion equations. Different equations dissemination of fourth order is proposed to improve the filtering performance. However, the diffusion equations of order four suffer from the problems of on-smoothing of the staircase effect and a very slow convergence towards the solution. In this paper, a hybrid equation that uses a convex combination to associate the second-order equation of Wang in the equation of the fourth-order Lysaker is proposed to reap the benefits of two equations combined for restoring noisy images. In this hybrid equation, a diffusion coefficient which uses the Z-shaped fuzzy logic membership function is proposed to limit the diffusion of strong gradients produced by the equation of the second order Wang et al for better preserve the contours of the image. The experimental results illustrate the effectiveness of the proposed model in image restoration.

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[29]. Yu, H., & Chua, C. S. (2006). GVF-based anisotropic diffusion models. IEEE Transactions on Image Processing, 15(6), 1517-1524. https://doi.org/ 10.1109/TIP.2006.871143

[30]. Zhang, L., Zhang, L., Mou, X., & Zhang, D. (2012, September). A comprehensive evaluation of full reference image quality assessment algorithms. In 2012 19th IEEE International Conference on Image Processing (pp. 1477-1480). IEEE. https://doi.org/ 10.1109/ICIP.2012.6467150

[31]. Zhong, P., & Wang, R. (2014). Jointly learning the hybrid CRF and MLR model for simultaneous denoising and classification of hyperspectral imagery. IEEE Transactions on Neural Networks and Learning Systems, 25(7), 1319-1334. https://doi.org/10.1109/TNNLS.20 13.2293061

[32]. Zhong, S., & Cherkassky, V. (2000, September). Image denoising using wavelet thresholding and model selection. In Proceedings 2000 International Conference on Image Processing (Cat. No. 00CH37101) (Vol. 3, pp. 262-265). IEEE.

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Research Paper

MRI Brain Tumor Locating using Neural Networks

Pavithra R*

Department of Electronics and Communication Engineering, Government College of Engineering, Tirunelveli, Tamil Nadu, India.

Pavithra, R. (2020). MRI Brain Tumor Locating using Neural Networks. i-manager's Journal on Image Processing , 7(1), 35-39. https://doi.org/10.26634/jip.7.1.17098

Abstract

Brain tumor identification is difficult task in the early stage of life. But now it has been improved with various machine learning algorithms. Now-a-days issue of brain tumor automatic identification is of great curious. In Order to discover the brain tumor of a patient, the data like MRI images of a patient's brain is used. Here our problem is to identify whether tumor is present or not in patients brain. It is very important to detect the tumors at starting level for a healthy life of patient. There are many literatures on discovering these kinds of brain tumors and improving the detection accuracies. In this paper, we estimate the brain tumor seriousness using Convolutional Neural Network(CNN) algorithm, which gives us accurate results.

References

(1). Eleyan, A. (2017). Simple and novel approach for image representation with application to face recognition. International Journal of Intelligent Systems and Applications in Engineering, 5(3), 89-93.

(2). Eleyan, A., & Demirel, H. (2011). Co-occurrence matrix and its statistical features as a new approach for face recognition. Turkish Journal of Electrical Engineering & Computer Sciences, 19(1), 97-107. https://doi.org/ 10.3906/elk-0906-27

(3). Giraddi, S., Pujari, J., & Seeri, S. (2015). Role of GLCM features in identifying abnormalities in the retinal images. International Journal of Image, Graphics and Signal Processing, 7(6), 45-51. https://doi.org/10.5815/ ijigsp.2015.06.06

(4). Kaldera, H. N. T. K., Gunasekara, S. R., & Dissanayake, M. B. (2019, March). Brain tumor classification and segmentation using faster R-CNN. In 2019 Advances in Science and Engineering Technology International Conferences (ASET) (pp. 1-6). IEEE. https://doi.org/10.1109/ICASET.2019.8714263

(5). Mohanaiah, P., Sathyanarayana, P., & GuruKumar, L. (2013). Image texture feature extraction using GLCM approach. International Journal of Scientific and Research Publications, 3(5), 1-5.

(6). Mohsen, H., El-Dahshan, E. S. A., El-Horbaty, E. S. M., & Salem, A. B. M. (2018). Classification using deep learning neural networks for brain tumors. Future Computing and Informatics Journal, 3(1), 68-71. https://doi.org/10.1016 /j.fcij.2017.12.001

(7). Othman, M. F. B., Abdullah, N. B., & Kamal, N. F. B. (2011, April). MRI brain classification using support vector machine. In 2011 Fourth International Conference on Modeling, Simulation and Applied Optimization (pp. 1-4). IEEE. https://doi.org/10.1109/ICMSAO.2011.5775605

(8). Pathak, B., & Barooah, D. (2013). Texture analysis based on the gray-level co-occurrence matrix considering possible orientations. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2(9), 4206-4212.

(9). Shree, N. V., & Kumar, T. N. R. (2018). Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network. Brain Informatics, 5(1), 23-30. https://doi.org/10.10 07/s40708-017-0075-5

(10). Sompong, C., & Wongthanavasu, S. (2016, July). Brain tumor segmentation using cellular automata-based th fuzzy c-means. In 2016 13 International Joint Conference on Computer Science and Software Engineering (JCSSE) (pp. 1-6). IEEE. https://doi.org/ 10.1109/JCSSE.2016.7748902

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Research Paper

Support Vector Machine for Classification of Spinal Cord Tumor

Geetha R.* , Mohan J.**

* ** Department of Electronics and Communication Engineering, SRM Valliammai Engineering College, SRM Nagar, Tamil Nadu.

Geetha, R., and Mohan, J. (2020). Support Vector Machine for Classification of Spinal Cord Tumor. i-manager's Journal on Image Processing , 7(1), 40-44. https://doi.org/10.26634/jip.7.1.16741

Abstract

This paper is a development of an algorithm to classify the spinal cord tumor in MRI images based on medical image processing. Most of researches involve deep learning algorithms to solve classification problem and is approached to detect tumor. From these considerations, approach of SVM for spinal cord tumor classification has been proposed. The method is improving the accuracy, sensitivity, specificity for the given spinal cord image. In pre-processing method, the spinal cord image is denoised using NLM filter, After image is denoised, Convolutional Neural Network(CNN) has been applied to extract the features from the segmented spinal cord image, which is obtained the texture details and used to identified the matched image. Support Vector Machine (SVM) is used for image classification. Finally, our proposed system is applied with a total of 500 images, including four kinds of spinal cord tumor images and our research experiment provides maximum value of the average accuracy, sensitivity, specificity are 98.1 (Astrocytomas), 98.9 (Astrocytomas), 97.17 (Hemangioblastomas) respectively. Minimum values of the accuracy, sensitivity, specificity are 93.8 (Ependymomas), 95.9 (Hemangioblastomas), 95.04 (Meningiomas) respectively.

References

(1). Bampis, C. G., Bovik, A. C., Markey, M. K., & Webb, K. M. (2016, March). Segmentation and extraction of the spinal canal in sagittal MR images. In 2016 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) (pp. 5-8). IEEE. https://doi.org/10.1109/SSIAI.2016.7459161

(2). Chmelik, J., Jakubicek, R., Walek, P., Jan, J., Ourednicek, P., Lambert, L., Amadori, E., & Gavelli, G. (2018). Deep convolutional neural network-based segmentation and classification of difficult to define metastatic spinal lesions in 3D CT data. Medical Image Analysis, 49, 76-88. https://doi.org/10.1016/j.media.2018. 07.008

(3). Eidelson, S. G. (2019). Spinal Tumors Center: Spineuniverse. Retrieved from https://www.SpineUniverse .com/ Conditions/Spinal-Tumors

(4). Li, Y., Zhou, W., Lv, G., Luo, G., Zhu, Y., & Liu, J. (2018, October). Classification of bone tumor on ct images using deep convolutional neural network. In International Conference on Artificial Neural Networks (pp. 127-136). Springer, Cham. https://doi.org/10.1007/978-3-030-01421- 6_13

(5). Mahmud, A. A., Karim, A. H. M. Z., Mou, F. A., & Rashedur, S. (2016). Spine tumor detection using MRI. International Journal of Biomedical Materials Research, 4(3), 35-42. https://doi.org/10.11648/j.ijbmr.20160403.15

(6). Major, D., Hladůvka, J., Schulze, F., & Bühler, K. (2013). Automated landmarking and labeling of fully and partially scanned spinal columns in CT images. Medical Image Analysis, 17(8), 1151-1163. https://doi.org/10.1016/j.me dia.2013.07.005

(7). Toghraee, M., Toghraee, M., Silvoster, M. L., & Rad, F. (2018). Design CNN on bone spine segmention to methods image processing. Journal of Electronics and Communication Systems, 3(2), 1-12.

i-manager's Journal on Image Processing (JIP)

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Volume 7 Issue 1 January - March 2020

An Improved Hardware-Oriented Level Set (Hols) for Complex Image Segmentation

Souleymane Balla-Arabe*

State key Laboratory of Integrated Services Networks, Xi’an, China.

Balla-Arabé, S. (2020). An Improved Hardware-Oriented Level Set (HOLS) for Complex Image Segmentation. i-manager's Journal on Image Processing , 7(1), 1-14. https://doi.org/10.26634/jip.7.1.17087

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Achieving Image Compression in Medical Imaging using Baseline Approach

Jimoh Abdul Ganiyu* , Ajayi Olusola Olajide**, Orimoloye Segun Michael ***, Aju Omojokun Gabrielis****

* - **** Department of Computer Science, Adekunle Ajasin University, Akungba–Akoko, Ondo, Nigeria.

Ganiyu, J. A., Olajide, A. O., Michael, O. S., and Gabrielis, A. O. (2020). Achieving Image Compression in Medical Imaging using Baseline Approach. i-manager's Journal on Image Processing , 7(1), 15-23. https://doi.org/10.26634/jip.7.1.16918

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Hybrid Equation Resulting from the Combination of a Second-Order Equation with a Fourth-Order Equation for the Restoration of Noisy Images

Simo Thierry* , Ntsama Eloundou Pascal**, Noura Alexendre***

* - *** Department of Physics, University of Ngaoundere, Ngaoundere, Cameroon.

Thierry, S., Pascal, N. E., and Alexendre, N. (2020). Hybrid Equation Resulting from the Combination of a Second-Order Equation with a Fourth-Order Equation for the Restoration Noisy Images. i-manager's Journal on Image Processing , 7(1), 24-34. https://doi.org/10.26634/jip.7.1.17038

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MRI Brain Tumor Locating using Neural Networks

Pavithra R*

Department of Electronics and Communication Engineering, Government College of Engineering, Tirunelveli, Tamil Nadu, India.

Pavithra, R. (2020). MRI Brain Tumor Locating using Neural Networks. i-manager's Journal on Image Processing , 7(1), 35-39. https://doi.org/10.26634/jip.7.1.17098

Abstract

References

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Support Vector Machine for Classification of Spinal Cord Tumor

Geetha R.* , Mohan J.**

* ** Department of Electronics and Communication Engineering, SRM Valliammai Engineering College, SRM Nagar, Tamil Nadu.

Geetha, R., and Mohan, J. (2020). Support Vector Machine for Classification of Spinal Cord Tumor. i-manager's Journal on Image Processing , 7(1), 40-44. https://doi.org/10.26634/jip.7.1.16741

Abstract

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Jimoh Abdul Ganiyu* , Ajayi Olusola Olajide, Orimoloye Segun Michael *, Aju Omojokun Gabrielis****

Simo Thierry* , Ntsama Eloundou Pascal, Noura Alexendre*