Removal of Pectoral Muscles and Locating Cancer in Breast using Fuzzy Technique

Volume 5 Issue 4 October - December 2018

Research Paper

Removal of Pectoral Muscles and Locating Cancer in Breast using Fuzzy Technique

C. Naga Raju*, A. Hima Bindu**

* Associate Professor and Head, Department of Computer Science and Engineering, YSR Engineering College of Yogivemana University,Proddatur, Andhra Pradesh, India.

**Research Scholar, Department of Computer Science and Engineering, Ryalaseema University, Kurnool, Andhra Pradesh, India.

Raju, C. N., & Bindu, A. H.(2018). Removal Of Pectoral Muscles And Locating Cancer In Breast using Fuzzy Technique. i-manager's Journal on Image Processing, 5(4),17-25. https://doi.org/10.26634/jip.5.4.15422

Abstract

The Micro-calcification which is an early sign of breast cancer is hard to find due to its small size, poor contrast, and blurry image boundary. Pectoral Muscles on mammograms are soft tissues of the body other than breast muscles, which looks like a cancer. The fuzzy algorithms used in this scenario are fuzzy opertors that analyze the image at pixel level to detect abnormalities and identify the location of abnormalities on the breast and pectoral muscles. This paper describes a technique which consist of five steps to find location of cancers in breast by removing pectoral muscles 1) To enhance the quality of poor breast images 2) preconisation of the breast shape 3) extract the cancer part from the breast images 4) removing the Pectoral muscles depends on the orientation of the breasts 5) location of the cancer part on breast images. The result shows the possibility and adequacy of the proposed approach.

Comparative Evaluation of Nature-Based Optimization Algorithms for Feature Selection on Some Medical Datasets

Volume 5 Issue 4 October - December 2018

Research Paper

Comparative Evaluation of Nature-Based Optimization Algorithms for Feature Selection on Some Medical Datasets

Ali Muhammad Usman *, Ali Usman Abdullah**, Alhassan Adamu***, Musa M Ahmed****

*-** Department of Computer Science, Federal College of Education (Tech.), Gombe, Nigeria.

*** Department Computer Science, Kano State University of Technology, Wudil, Nigeria.

**** Lecturer, Department of Physical Science Education, MAUTECH, Yola, Nigeria.

Usman, A.M., Abdullah, A.U., Adamu, A., & Ahmed, M.M.(2018). Comparative Evaluation of Nature-Based Optimization Algorithms for Feature Selection on Some Medical Datasets.i-manager's Journal on Image Processing, 5(4), 9-16.https://doi.org/10.26634/jip.5.4.15938

Abstract

Nowadays, the veracity, velocity, values, and size of data are growing exponentially. In fact, the data is growing beyond the capacity of current hardware facilities. This resulted in high cost of storing the data. Perhaps, some of the data stored are not very useful and create problems when mining the data, to make some sense out of it. Feature selection is a step forward towards reducing the unnecessary huge amount of the stored data. In this study, Flower Pollination Algorithm (FPA) along with its binary version (BFPA) are used for feature selection on some medical datasets. The results obtained is in favor of the BFPA with better classification accuracy of over 90% on some of the datasets and fewer number of features compared to FPA, improved harmony search with rough set together with particle swarm optimization with rough set. Hence, the experimental results demonstrate the efficiency and effectiveness of BFPA as the best technique among the evaluated methods for feature selection particularly on medical datasets.

Brightness Enhancement Technique for Video Frame Improvement Based on Pixel Intensity Analysis

Volume 5 Issue 4 October - December 2018

Research Paper

Brightness Enhancement Technique for Video Frame Improvement Based on Pixel Intensity Analysis

H. A. Abdulkareem *, A. M. S. Tekanyi**, I. Yau***, K. A. Abu- Bilal****, H. Adamu*****

*-*****Ahmadu Bello University, Zaria, Nigeria.

Abdulkareem, H. A., Tekanyi, A. M. S., Yau, I., Abu-Bilal, K. A.,& Adamu, H.(2018). Brightness Enhancement Technique For Video Frame Improvement Based On Pixel Intensity Analysis. i-manager's Journal on Image Processing, 5(4), 1-8.https://doi.org/10.26634/jip.5.4.15937

Abstract

This study developed a brightness enhancement technique for video frame pixel intensity improvement. Frames extracted from the six sample video data used in this work were stored in the buffer as images. Noise was added to the extracted image frames to vary the intensity of their pixels so that these pixel values of noisy images differ from their true values in order to determine the efficiency of the developed technique. Simulation results of this paper showed improvement in pixel intensity and histogram distribution. The Peak to Signal plus Noise Ratio evaluation showed that the efficiency of the developed technique for both grayscale and coloured video frames were improved by PSNR of 12.45%, 16.32%, 27.57%, and 19.83% over those of the grey level colour (black and white) images for the NAELS1.avi, NAELS2.avi, NTA1.avi, and NTA2.avi, respectively. Also, a percentage improvement of 28.93% and 31.68% were obtained for the coloured images over the grey level images for Akiyo.avi and Forman.avi benchmark video frames, respectively.

A Comparative Analysis of Leaf Disease Detection using Image Processing Technique

Volume 5 Issue 3 July - September 2018

Review Paper

A Comparative Analysis of Leaf Disease Detection using Image Processing Technique

C. M. Samiha*, S. P. Pavan Kumar **

* PG Scholar, Department of Computer Science and Engineering, Sri Jayachamarajendra College of Engineering, Mysuru, Karnataka, India.

** Assistant Professor, Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India.

Samiha, C.M., and Kumar, P.S.P., (2018). A Comparative Analysis of Leaf Disease Detection Using Image Processing Technique. i-manager’s Journal on Image Processing, 5(3), 40-46. https://doi.org/10.26634/jip.5.3.15044

Abstract

The essential part of any ecosystem is plant. All the organisms get energy from plants directly or indirectly. It is important to identify the disease in plant parts like leaf, stem, and fruit. Leaf diseases are caused by virus, bacteria, etc. Normally, a farmer identifies the leaf disease by observing spots, color, and shape of the leaf, but sometimes they take help from the experts to detect diseased leaf or crops. The manual detection of disease is less accurate and complex. Image processing techniques help farmers for timely detection of the diseases. K-Nearest Neighbor (KNN), K-Means clustering, Support Vector Machine (SVM), Artificial Neural Network (ANN), and various segmentation algorithm and classifiers are used for detection and classification of leaf diseases. In this paper, various diseases that occur in parts of the plant and identification of leaf diseases were discussed.

Blood Leukemia Detection using Neural Networks and Fuzzy Logic: A Survey and Taxonomy

Volume 5 Issue 3 July - September 2018

Review Paper

Blood Leukemia Detection using Neural Networks and Fuzzy Logic: A Survey and Taxonomy

Fameshwari Deshmukh*, Amar Kumar Dey**

*PG Scholar, Department of Electronics & Telecommunication, Bhilai Institute of Technology, Durg, Chhattisgarh, India.

**Assistant Professor, Department of Electronics and Telecommunication Engineering, Bhilai Institute of Technology, Durg, Chhattisgarh,India.

Fameshwari, and Dey, A.K., (2018). Blood Leukemia Detection Using Neural Networks And Fuzzy Logic: A Survey And Taxonomy. i-manager’s Journal on Image Processing, 5(3), 34-39. https://doi.org/10.26634/jip.5.3.14984

Abstract

Blood cancer or leukemia detection using microscopic images is a challenging task considering the fact that variations in blood cell patterns are miniscule in nature and human detection may be prone to errors due to inherent deficiencies or anomalies in the dataset or due to human errors. Hence using automated classification has been considered using data pre-processing techniques such as Artificial Neural Networks and Fuzzy Logic. Recently, a new domain of research called neuro-fuzzy systems has garnered a lot of attention due to its efficacy. This paper introduces the challenges faced in the detection and classification of blood leukemia. Along with it, the paper focuses on the various significant contributions in the field by different researchers. This may pave the path for further improvement in accuracy of classification of leukemia.

LM, RP, and GD Based ANN architecture models for Biomedical Image Compression

Volume 5 Issue 3 July - September 2018

Research Paper

LM, RP, and GD Based ANN architecture models for Biomedical Image Compression

G. Vimala Kumari *, G. Sasibhushana Rao**, B. Prabhakara Rao***

* Assistant Professor, Department of Electronics and Communication Engineering, MVGR College of Engineering, Vizianagaram,Andhra Pradesh, India.

**Professor, Department of Electronics & Communication Engineering, Andhra University College of Engineering, Visakhapatnam,Andhra Pradesh, India.

*** Programme Director, School of Nanotechnology, JNTU, Kakinada, Andhra Pradesh, India.

Kumari, V.G., Rao, S.G., and Rao, p.B., (2018). LM, RP AND GD Based Ann Architecture Models For Bio Medical Image Compression. i-manager’s Journal on Image Processing, 5(3), 21-33. https://doi.org/10.26634/jip.5.3.15195

Abstract

The aim of this paper is to present an image compression method using feedforward backpropagation neural networks. Medical imaging is an efficient source for better diagnosis of the disease and also helps in assessing the severity of the disease. But due to the increasing size of the medical images, transferring and storage of images require huge bandwidth and storage space. Therefore, it is essential to derive an effective compression algorithm, which have minimal loss, time complexity, and increased reduction in size. With the concept of Neural Network, data compression can be achieved by producing an internal data representation. The training algorithm and development architecture gives less distortion and considerable compression ratio and also keeps up the capability of hypothesizing and is becoming important. The performance metrics of three algorithms, Levenberg Marquardt algorithm, Resilient backpropagation algorithm, and Gradient Decent algorithm have been computed on Magnetic Resonance Imaging (MRI) images and it is observed that Levenberg Marquardt algorithm is more accurate when compared to the other two algorithms.

Upright FAST-Harris Filter

Volume 5 Issue 3 July - September 2018

Research Paper

Upright FAST-Harris Filter

Abdulmalik Danlami Mohammed*, Saliu Adam Muhammed**, Idris Mohammed Kolo***, Adama Victor Ndako****, Shafi’i Muhammed Abdulhamid*****, Abdulkadir Baba Hassan******, Abubakar Saddiq Mohammed*******

*_****Lecturer, Department of Computer Science, Federal University of Technology, Minna, Nigeria.

*****Senior Lecturer and Head, Department of Cyber Security Science, Federal University of Technology, Minna, Nigeria

*** ***Associate Professor, Department of Mechanical Engineering, Federal University of Technology, Minna, Nigeria

*******Department of Electrical/Electronic Engineering, Federal University of Technology, Minna, Nigeria

Mohammed, A.D., Saliu, A.M., Kolo, I.M., Ndako, A.V., Abdulhamid, S.M., Hassan, A.B., and Mohammed, A.S (2018). Color and Shape Based Automatic Detection of Pedestrians in Surveillance Videos. i-manager’s Journal on Image Processing, 5(3),14-20.https://doi.org/10.26634/jip.5.3.15689

Abstract

The traditional approaches to the classification of image regions suffer drawbacks in the face of imaging conditions (occlusion, illumination changes, rotation, viewpoint changes, and image blurring) and thus contribute to the poor performance of several vision based applications, such as object recognition, object tracking, image retrieval, pose estimation, camera calibration, 3D reconstruction, Structure from motion, stereo images, and image stitching. However, in this work, feature points extraction method by decomposition of image structure is employed in order to overcome these challenges. The decomposition of an image structure into feature set enhances the performance of many vision-based applications and system. The feature point extraction method which we refer to as Upright Feature from Accelerated Segment Test with Harris filter (UFAH) in this text, works by combining Feature from Accelerated Segment Test detector with Harris filter. The result obtained in the evaluation process shows that UFAH is robust and also invariant to imaging conditions (i.e rotation, illumination changes, and image blurring).