Skin Texture Recognition through Image Processing

Volume 6 Issue 1 January - March 2019

Research Paper

Skin Texture Recognition through Image Processing

Sukhdeep Sharma *, Aayushi Dubey**

*-**Department of Computer Science and Engineering, Manav Rachna International Institute of Research and Studies, Haryana, India.

Sharma, S., & Dubey, A. (2019). Skin Texture Recognition through Image Processing. i-manager's Journal on Image Processing,6(1), 17-25. https://doi.org/10.26634/jip.6.1.15560

Abstract

Image processing is done to get a better version of an image or an enhanced or filtered image. It is used to extract important information from the image. A raw image is converted into a digital image by undergoing various processes for which proper algorithms and mechanisms are defined. To further investigate on an image, we can perform texture analysis on it. Texture recognition is also an important characteristic of image processing. Both texture recognition and image processing have a variety of uses in the medicine field. In this paper, the authors have discussed about how we can detect the texture of our skin and use it further to test the suitability of skin products that we use daily.

Alzheimer’s Stage Classification using SVM Classifier using Brain MRI Texture Features

Volume 6 Issue 1 January - March 2019

Research Paper

Alzheimer’s Stage Classification using SVM Classifier using Brain MRI Texture Features

Shaik Basheera*, M. Satya Sai Ram**

* Department of Electronics and Communication Engineering, Acharya Nagarjuna University College of Engineering, Acharya Nagarjuna University, Guntur, India.

**Department of Electronics and Communication Engineering, RVR & JC College of Engineering, Guntur, Andhra Pradesh, India.

Basheera, S., & Ram, M. S. S. (2019). Alzheimer’s Stage Classification using SVM Classifier using Brain MRI Texture Features. i-manager's Journal on Image Processing, 6(1), 9-16. https://doi.org/10.26634/jip.6.1.15742

Abstract

This paper deals with the Brain disorder caused due to dementia, where the brain size gets effected and reduces its volume. Estimating the grade of Alzheimer's is a challenging task. Spatial texture information collected from T2 Weighted MRI images are used to perform classification and validation. The authors use 54 Brain MRI Slices, Gray Level Cooccurrence matrix is used to extract the attributes, and those are used to train the classifiers. On comparing Support Vector Machine (SVM) with Naïve Bayes classifier and KNN, SVM gives good classification accuracy of 98.1%. The classifiers classify the MRI into AD, MCI, and CN. Five independent images are collected from the Internet sources, by testing those images using SVM and correlating with clinical data of those images, it achieves 100% accuracy.

Optimized Video Compression Using Modified Intelligent Behaviour of Firefly Algorithm

Volume 6 Issue 1 January - March 2019

Research Paper

Optimized Video Compression Using Modified Intelligent Behaviour of Firefly Algorithm

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.(2019). Optimized Video Compression Using Modified Intelligent Behaviour of Firefly Algorithm. i-manager's Journal on Image Processing, 6(1), 1-8.https://doi.org/10.26634/jip.6.1.16363

Abstract

Transformation in mobile networks and multimedia communications make image and video compression important aspects of digital image processing. The main aim of image or video compression is to reduce the size of the image or video (redundancy) with little or no degradation of quality for an effective transmission and storage. This paper presents an optimized video compression using modified intelligent behavior of firefly algorithm. A total of six (four acquired and two benchmark) sample video data were used to implement the achieved technique. Frames were extracted from the video data and stored in the form of images in a buffer. Compression of the video frames was achieved by reducing the effect of pixel intensity with larger distance part. This was identified as one of the shortcomings with the Firefly Optimization Algorithm (FOA) method of image compression. In this paper, the impact of the modification was clearly shown using the Peak Signal to Noise Ratio (PSNR). The modification was achieved by including the root mean square in the standard equations of the FOA. In order to reduce the effect of pixel intensity with larger distance part, this was identified as one of the shortcomings. When the image samples were subjected to the (mFOA) compression technique, a same amount of improvement was achieved. Simulation results indicated that the mFOA technique outperformed the FOA method. The PSNR evaluation showed an improved reduction of frame size by 7.34%, 3.30%, 4.90%, and 5.75% for respective NAERLS1.avi, NAERLS2.avi, NTA1.avi, and NTA2.avi captured benchmark video frames and also 3.56% and 3.86% for respective video frames of Akiyo.avi and Forman.avi

Estimation of Hybrid Image Compression Algorithms for Underwater Images

Volume 5 Issue 4 October - December 2018

Research Paper

Estimation of Hybrid Image Compression Algorithms for Underwater Images

R. Pandian *, S. Lalitha Kumari **, R. Raja Kumar***, D. N. S. Ravikumar****

*-** Associate Professor, Department of Electronics and Instrumentation Engineering, Sathyabama Institute of Science and Technology,Chennai, Tamil Nadu, India.

*** Professor, Department of Mathematics, Sathyabama Institute of Science and Technology, Chennai, TamilNadu, India.

**** Assistant Professor, Department of Electrical and Electronics Engineering, Sathyabama Institute of Science and Technology, Chennai,Tamil Nadu,India.

Pandian, R., Kumari, S. L., Kumar, R. R., & Ravikumar, D. N. S.(2018). Estimation of Hybrid Image Compression Algorithms for Underwater Images. i-manager's Journal on Image Processing, 5(4), 34-38. https://doi.org/10.26634/jip.5.4.15209

Abstract

Image compression techniques find a wide role in the field of underwater image processing. The wavelet based image compression algorithm performance mainly depends on the encoding methods adopted. In this work, symlet wavelet and hybrid encoding techniques such as Set Partitioning Hierarchy Tree and Huffman encoding are applied on an underwater image and the performance is estimated by compression ratio and PSNR. The results clearly indicate that hybrid encoding performed well. In order to assess its value, PSNR and CR are calculated. The best compression algorithm is chosen based on a compromise between PSNR and CR. The underwater images are compressed in this work.

An Evaluation and Comparison of SVM and Neural Classifier for Breast Cancer Detection Utilizing Contourlet and Discrete Wavelet Transforms

Volume 5 Issue 4 October - December 2018

Research Paper

An Evaluation and Comparison of SVM and Neural Classifier for Breast Cancer Detection Utilizing Contourlet and Discrete Wavelet Transforms

Soumya Hundekar *, Saritha Chakrasali**

* PG Scholar, Department of Information Science and Engineering, BNM Institute of Technology, Bangalore, Karnataka, India.

** Professor, Department of Information Science and Engineering, BNM Institute of Technology, Bangalore, Karnataka, India.

Hundekar, .S., & Chakrasali, S. (2018). An Evaluation and Comparison of SVM and Neural Classifier for Breast Cancer Detection Utilizing Contourlet and Discrete Wavelet Transforms. i-manager's Journal on Image Processing, 5(4), 26-33.https://doi.org/10.26634/jip.5.4.15394

Abstract

The aim of this work is to efficiently detect breast cancer at an early stage and reduce the death rates of women. The purpose of this work is to identify the tumor present in the breast region of mammogram image as benign or malignant as these images are generally of low quality and sometimes radiologists need to seek second opinion to come to the conclusion that cancer is present. The image processing procedure is applied to detect breast cancer from mammographic ROI image. Earlier doctors used MRI, CT-scan, Ultrasound techniques to detect breast cancer, using which it was difficult to identify cancerous tumour at an early stage. The proposed methodology uses mammography technique to identify the tumor present in the breast region. The discrete wavelet and Contourlet transforms are used to decompose the given gray-scale image. The statistical and textual features are being extracted from the coefficients of spatial domain along with frequency domain values. The classification of mammographic ROI image is performed using support vector and artificial neural classifiers. The tool used in this work is Matlab. This work is recommended to study for all those working on breast cancer area of an image processing domain.

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.