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


Volume 3 Issue 1 January - March 2016 [Open Access]

Research Paper

Cuckoo Search Framework For Feature Selection And Classifier Optimization In Compressed Medical Image Retrieval

Reddi Kiran Kumar* , Vamsidhar Enireddy**
*Assistant Professor, Department of Computer Science and Engineering, Krishna University, Machilipatnam, Andhra Pradesh, India
**Research Scholar, Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Kakinada, India.
Reddi, K. K., and Enireddy, V. (2016). Cuckoo Search Framework For Feature Selection And Classifier Optimization In Compressed Medical Image Retrieval. i-manager's Journal on Image Processing, 3(1), 1-12.

Abstract

With the availability of different medical imaging equipment for diagnoses, medical professionals are increasingly depending on the computer aided techniques for retrieving similar images from large repositories. This work investigates medical image retrieval problem for lossless compressed images. Lossless compression technique is utilized for compressing the medical images for easy transmission and storage. Texture features are extracted using Gabor filters, Shape features using the Gabor - shape and best features of these are selected by using a novel Cuckoo Search algorithm and compared with other statistical techniques. Classification was done by using the Recurrent neural Network. Optimization of the neural network is done using the Cuckoo Search. Experimental results show the advantages of the proposed framework.

Research Paper

Hybrid Wavelet based Approach for Image De-Noising through PCA

Gunjan Seth* , Sukhvir Kaur**, Jagdeep Singh***
* PG Scholar, Department of Computer Science and Engineering, CT Institute of Engineering Management & Technology, Jalandhar, India.
** Assistant Professor, Department of Computer Science and Engineering, CT Institute of Engineering Management & Technology, Jalandhar, India.
*** Assistant Professor, Department of Electronics and Communication Engineering, National Institute of Technology, Jalandhar, India.
Sethi, G., Kaur, S., and Singh, J. (2016). Hybrid Wavelet Based Approach For Image De-Noising Through PCA. i-manager's Journal on Image Processing, 3(1), 13-19.

Abstract

De-Noising is a crucial problem for various types of image in the digital image processing. The main objective is to be fade away the noise factor by transfiguring into realistic Image as well as safeguarding the real quality and structure of the Image. Much hardware equipment such as digital electronic devices may suffer some issues that are noisy and blurred images due to degradation in the quality of the visioning image. These noisy images and blur images come under the problem of less information about the working object in a capturing environment. In this paper, the De-Noising technique has been proposed at different standard deviation for each processed image to check that at what level of noise it may work. In this proposed technique, wavelet is applied to a Noisy Image and further on the decomposed sections, SPG-PCA is used for quality enhancement. It consists of two stages: image estimation by removing the noise and further refinement of the first stage. Noise is removed at the maximum extent in first stage and the application of NPG improves the visualization of the De-Noised Image. A different standard deviation helps to optimize the original image which is based on the De-Noising scheme using quality matrices. The proposed technique can also be applied on satellite images, television pictures, medical images, etc. In this research work optimized De-Noising matrices like PSNR, SSIM, Maximum Difference and Normalized Cross-Correlation for the Dataset. Experimental results show a much improved performance of the proposed filters in the presence of Gaussian noise that are analyzed and illustrated.

Research Paper

Multispectral Image Compression with High Resolution Improved SPIHT for Testing Various Input Images

V. Bhagya Raju,* , K. Jaya Sankar**, C. D. Naidu***, Srinivas Bachu****
* Research Scholar, Department of Electronics and Communication Engineering, JNTU Hyderabad, Telangana, India.
** Professor and Head, Department of ECE , Vasavi College of Engineering, Hyderabad, Telangana, India.
*** Principal, VNR Vignana Jyothi Institute of Enineering & Technology, Hyderabad, Telangana, India.
**** Associate Professor, Department of ECE, Guru Nanak Institutions Technical Campus, Telangana, India.
Raju, V.B., Sankar, K.J., Naidu, C.D., and Bachu, S. (2016). Multispectral Image Compression With High Resolution Improved Spiht For Testing Various Input Images. i-manager's Journal on Image Processing, 3(1), 20-28.

Abstract

Due to the current development of Multispectral sensor technology, the use of Multispectral images has become more and more popular in recent years in remote sensing applications. This paper exploits the spectral and spatial redundancies that exist in different bands of multispectral images and effectively compresses these redundancies by means of a lossy compression method while preserving the crucial and vital spectral information of objects that prevails in the multispectral bands. In this paper, interpolated super resolution transform based DWT with Improved SPIHT algorithm for various multispectral datasets has been proposed. The proposed algorithm, a lossy multispectral image compression method yields better performance results for PSNR and Compression Ratio with sym8 wavelet when compared with previous well-known compression methods and existing discrete wavelets.

Research Paper

Analysis of Iris Segmentation using Circular Hough Transform and Daughman's Method

Divya Ann Roy* , Urmila S. Soni**
* PG Scholar, Department of Electronics and Communication Engineering, CSIT, Durg, Chhattisgarh, India.
** Associate Professor, Department of Electronics and Communication Engineering, CSIT, Durg, Chhattisgarh, India.
Roy, D.A., and Soni, U.S. (2016). Analysis of Iris Segmentation using Circular Hough Transform and Daughman's Method. i-manager’s Journal on Image Processing, 3(1), 29-36.

Abstract

Iris recognition is a special type of biometric system, which is used to identify a person by analyzing the patterns in the iris. It is used to recognize the human identity through the textural characteristics of one's iris muscular patterns. Although eye colour is dependent on heredity, iris is independent even in the twins. Out of various biometrics such as finger and hand geometry, face, ear and voice recognition, iris recognition has been proved to be one of the most accurate and reliable biometric modalities because of its high recognition. Iris recognition involves 5 major steps. Firstly, image acquisition is done in which the image is captured by a high resolution camera, then the iris and the pupillary boundary are extracted out from the whole eye image, which is called segmentation. After segmentation, the circular dimension is converted to a fixed rectangular dimension which is called normalization. From this normalised image, the feature is extracted from Gabor filter, DFT, FFT, etc. At last, the iris code is matched using Hamming distance and Euclidean method. This project focuses on iris segmentation. Iris segmentation is the most important part in the iris recognition process because the areas that are wrongly considered as the iris regions would corrupt the biometric templates resulting in a very poor recognition [16]-[21]. The main objective of iris segmentation is to separate the iris region from the pupil and sclera boundaries. There are various methods for segmenting the iris from an eye image to give a best segmented result. In this project, iris segmentation is done using Daugman's integro differential method and Circular Hough Transform to find out the pupil and the iris boundaries. Iris images are taken from the CASIA V4 database, and the iris segmentation is done using Matlab software where iris and pupilary boundaries are segmented out. The experimental result shows that 84% accuracy is obtained by segmenting the iris by Circular Hough Transform and 76% accuracy is obtained by segmenting the iris through Daughman's method. It is concluded that, the Circular Hough Transform method of iris recognition is more accurate than the Daughman's method.

Research Paper

Feature Segmentation of Blood Vessels using Active Contour with Hybrid Region Information with Application to Retinal Images

Suri Sahithi* , G. Guru Prasad**
* PG Scholar, Department of Electronics and Communication Engineering, Sree Vidyanikethan Engineering College, Tirupathi, India.
** Assistant Professor, Department of Electronics and Communication Engineering, Sree Vidyanikethan Engineering College, Tirupathi, India.
Sahithi, S., and Prasad, G.G. (2016). Feature Segmentation of Blood Vessels using Active Contour with Hybrid Region Information with Application to Retinal Images. i-manager’s Journal on Image Processing, 3(1), 37-43.

Abstract

Computerized detection of blood vessel structures is becoming one of the most interesting parts in the field of diagnosis of the vascular diseases. The objective of this paper is to introduce a novel filter, based on a new kernel function with Cauchy distribution to improve the accuracy of the automated retinal vessel detection. Moreover, for a good segmentation performance, the proposed model has the benefit of using distinct types of region information. The aim of the proposed model is to increase the accuracy of an image.