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


Volume 2 Issue 4 October - December 2015

Research Paper

An Estimation of Human Age Group Based on Facial Edge Image Patterns

Gandu Bharat* , Burusu Rajesh Kumar**
* Assistant Professor, Department of Electronics and Communication Engineering, Nagaland University, Dimapur, Nagaland.
** Assistant Professor, Department of Computer Science and Engineering, Malla Reddy Engineering College, Hyderabad, TS, India.
Bharat, G., and Kumar, B.R. (2015). An Estimation of Human Age Group Based on Facial Edge Image Patterns. i-manager’s Journal on Image Processing, 2(4), 1-9. https://doi.org/10.26634/jip.2.4.3685

Abstract

The present paper derives a new approach for estimating the age group of a person based on structural patterns of an edge image identified in a human face image. This approach uses canny edge operator algorithm for extracting the edges of the image because canny edge operator gives more edges. In this approach, edges are most important because, wrinkles are formed on face when age is growing. When wrinkles are formed on facial image, automatically more edge information is available. This approach uses a structural concept. The present study derived four distinct structural patterns on each 3x3 sub window of facial edge image i.e. Right Diagonal Pattern (RDP), Left Diagonal Pattern (LDP), Vertical Central Line Pattern (VCLP) and Horizontal Central Line Pattern (HCLP) pattern. The central pixel value of the 3x3 sub image is considerable in all four patterns. Based on formation of those four structural patterns, i.e. frequency of occurrences of structural patterns estimate the human age group into five age groups i.e. Child (0 to 9 years), Young (10 to 20 years), Young Adult (21-35), Adult (36 to 50 years) and Senior Age (>50 years). The efficiency of the proposed method is calculated by applying it on different huge facial databases like FgNET and Morph. The proposed method shows high rate of classification when compared with the other existing methods.

Research Paper

Image Denoising using Hybrid Filter in Presence of Multiple Noises and Graphical User Interface for Medical Image Enhancement

Gopi Karnam* , T. Ramashri**
* Assistant Professor, Department of Electronics and Communication Engineering, Sri Venkateswara University College of Engineering, Tirupati, Andhra Pradesh, India.
** Professor, Department of Electronics and Communication Engineering, Sri Venkateswara University College of Engineering, Tirupati, Andhra Pradesh, India.
Karnam, G. and Ramashri, T. (2015). Image Denoising using Hybrid Filter in Presence of Multiple Noises and Graphical User Interface for Medical Image Enhancementt. i-manager’s Journal on Image Processing, 2(4), 10-18. https://doi.org/10.26634/jip.2.4.3686

Abstract

In the field of image processing, the filtering algorithms are functional over the noisy images to eliminate the noise and protect the image details. In medical diagnosis, removing noise is a very challenging issue, as images are corrupted by multiple noises. Medical images like CT, MRI, and PET have information about the heart, nerves and brain. These images are to be precise and free from noise. This paper presents an efficient method for noise reduction, contrast enhancement for medical images. The projected method uses Hybridization of adaptive median filter with the wiener filter for denoising multiple noises. Wiener filter have enhanced stability between smoothness and precision. It also shows the GUI representation of Image smoothing, Histogram Equalization. The method is experimented on the MRI (Magnetic Resonance Image) and performance is evaluated in terms of the Peak Signal to Noise Ratio (PSNR), Correlation coefficient, Mean Absolute Error (MAE) and Mean Square Error (MSE). The proposed technique removes the Gaussian noise, Impulse noise and Blurredness in the images and improve the quality of images. The result shows that, the hybrid filter outperforms most of the basic algorithms for reduction of multiple noises in medical images. Finally, the results proved that the exploitation of hybrid filter gives the appropriate and consistent results on the test images and provide precision to picture while preserving its information.

Research Paper

A Computer Aided Diagnosis (CAD) System for Segmentation and Analysis of Brain Magnetic Resonance Images

T. Chandra Sekhar Rao* , G.Sreenivasulu**
* Professor, Department of Electronics and Communication Engineering, Loyola Institute of Technology and Management, Sattenapalli, India.
** Professor, Department of Electronics and Communication Engineering, SVU College of Engineering, Tirupati, India.
Rao, T.C.S., and Sreenivasulu, G. (2015). A Computer Aided Diagnosis (CAD) System for Segmentation and Analysis of Brain Magnetic Resonance Images. i-manager’s Journal on Image Processing, 2(4), 19-29. https://doi.org/10.26634/jip.2.4.3687

Abstract

Medical image processing has become the main stay of diagnosis for a multitude of diseased conditions. The advent of sophisticated image processing procedures coupled with the exponential growth in processing power of systems and storages has resulted in huge volume of data that has to be interpreted and analyzed. The huge volume data implies the need for automated analysis system to reduce the burden on radiologists and help in providing quality diagnosis. This paper presents a Computer Aided Diagnosis (CAD) system for segmentation and analysis of Brain tumors in magnetic resonance images. The system has scope for making different analysis like edge analysis, morphological processing, histogram analysis etc. As part of system four different segmentation approaches like, K means segmentation, Watershed segmentation; Fuzzy C Means (FCM) segmentation and Enhanced Independent Component Analysis (EICA) and Mixture model based segmentation are implemented. The performance of the segmentation approaches are evaluated using different performance measures.

Research Paper

Non Linear Robust Edge Detector for Noisy Images

Atluri Srikrishna* , B. Eswara Reddy**, M. Pompapathi***
* Professor and Head, Department of Information Technology at RVR & JC College of Engineering, Guntur, India.
** Professor, Department of Computer Science, JNTUA College of Engineering, Anantapur, India.
*** Assistant Professor, Department of Information Technology, RVR & JC College of Engineering, Guntur, India.
Srikrishna, A., Reddy, B.E., and Pompapathi, M. (2015). Non Linear Robust Edge Detector for Noisy Images. i-manager’s Journal on Image Processing, 2(4), 30-38. https://doi.org/10.26634/jip.2.4.3688

Abstract

Identification of edge pixels of the noisy signal without doing regularization is still a challenging problem for researchers. There exists different methods and each method has its own assumptions, advantages, and limitations. Author propose a Non Linear Robust Edge Detector (NLRED), which uses n x n window in order to detect the edges of all possible orientations at noisy images. The proposed method partitions the neighbors of the pixel which is under observation for edge candidature into two sub regions based on differences in the local gray level value. The proposed method calculates test statistic for pixel of each sub region by calculating mean, the placement of each member, index of variability and test statistics. The test statistics with maximum value is considered based on two sub regions. These statistics are calculated for eight different orientations. Among these, the statistic with minimum value is considered for edge candidature. The performance is measured in terms of Figure of Merit (FOM) to show the efficiency of the proposed method by comparing with statistical edge detector CANNY approach, in order to detect all possible edges.

Research Paper

Adaptive Thresholding for Porosity Measurement in Sand Particles

Maheswaran U* , S. Priyadharshini**
*_** Assistant Professor, Department of Electronics and Communication Engineering, Rajalakshmi Institute of Technology, Chennai, India.
Maheswaran, U., and Priyadharshini, S. (2015). Adaptive Thresholding for Porosity Measurement in Sand Particles. i-manager’s Journal on Image Processing, 2(4), 39-45. https://doi.org/10.26634/jip.2.4.3689

Abstract

In this paper, the authors evaluate the porosity of given sand images using image processing technique. An adaptive thresholding technique has been used to calculate this. It has been shown that, the flow and shear characteristics of granular particles such as soils are significantly dependent on the shape of the particles. This is important from a practical viewpoint because a fundamental understanding of granular behavior will lead to an improved understanding of soil stability and influence the design of structural foundations. Furthermore, the calculation of soil stability and, consequently, structural stability is particularly useful during earthquake events. In previous work, the author have demonstrated the applicability of X-ray and optical tomography measurements for characterizing 3-D shapes of natural sands and manufactured granular particles. In this paper, the authors name extended the work to measure the arrangement and the orientation of an assemblage of such particles. A combination of X-ray Computed Tomography (CT) for measuring the coordinates of the individual particles and an iterative adaptive thresholding technique for computing the local variations in porosity is employed to generate porosity maps. Such maps can be used to gain a more fundamental understanding of the shear characteristics of granular particles. In this paper, author demonstrate the success of technique by exercising the method on several sets of granular particles—glass beads (used as a control), natural Michigan Dune and Daytona Beach sand, and processed Dry #1 sand.