Artificial neural network-statistical approach for PET volume analysis and classification

Copyright © 2012 The Authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.This article has been made available through the Brunel Open Access Publishing Fund.The increasing number of imaging studies and the prevailing application of positron emission tomography (PET) in clinical oncology have led to a real need for efficient PET volume handling and the development of new volume analysis approaches to aid the clinicians in the clinical diagnosis, planning of treatment, and assessment of response to therapy. A novel automated system for oncological PET volume analysis is proposed in this work. The proposed intelligent system deploys two types of artificial neural networks (ANNs) for classifying PET volumes. The first methodology is a competitive neural network (CNN), whereas the second one is based on learning vector quantisation neural network (LVQNN). Furthermore, Bayesian information criterion (BIC) is used in this system to assess the optimal number of classes for each PET data set and assist the ANN blocks to achieve accurate analysis by providing the best number of classes. The system evaluation was carried out using experimental phantom studies (NEMA IEC image quality body phantom), simulated PET studies using the Zubal phantom, and clinical studies representative of nonsmall cell lung cancer and pharyngolaryngeal squamous cell carcinoma. The proposed analysis methodology of clinical oncological PET data has shown promising results and can successfully classify and quantify malignant lesions.This study was supported by the Swiss National Science Foundation under Grant SNSF 31003A-125246, Geneva Cancer League, and the Indo Swiss Joint Research Programme ISJRP 138866. This article is made available through the Brunel Open Access Publishing Fund

Subband domain coding of binary textual images for document archiving

Cataloged from PDF version of article.In this work, a subband domain textual image compression method is developed. The document image is first decomposed into subimages using binary subband decompositions. Next, the character locations in the subbands and the symbol library consisting of the character images are encoded. The method is suitable for keyword search in the compressed data. It is observed that very high compression ratios are obtained with this method. Simulation studies are presented

Development of Some Efficient Lossless and Lossy Hybrid Image Compression Schemes

Digital imaging generates a large amount of data which needs to be compressed, without loss of relevant information, to economize storage space and allow speedy data transfer. Though both storage and transmission medium capacities have been continuously increasing over the last two decades, they dont match the present requirement. Many lossless and lossy image compression schemes exist for compression of images in space domain and transform domain. Employing more than one traditional image compression algorithms results in hybrid image compression techniques. Based on the existing schemes, novel hybrid image compression schemes are developed in this doctoral research work, to compress the images effectually maintaining the quality

Dynamic Selection of Suitable Wavelet for Effective Color Image Compression using Neural Networks and Modified RLC

Image Compression has become extremely important today with the continuous development of internet, remote sensing and satellite communication techniques. In general, single Wavelet is not suitable for all types of images. This paper proposes a novel approach for dynamic selection of suitable wavelet and effective Image Compression. Dynamic selection of suitable wavelet for different types of images, like natural images, synthetic images, medical images and etc, is done using Counter Propagation Neural Network which consists of two layers: Unsupervised Kohonen (SOFM) and Supervised Gross berg layers. Selection of suitable wavelet is done by measuring some of the statistical parameters of image, like Image Activity Measure (IAM) and Spatial Frequency (SF), as they are strongly correlated with each other. After selecting suitable wavelet, effective image compression is done with MLFFNN with EBP training algorithm for LL2 component. Modified run length coding is applied on LH2 and HL2components with hard threshold and discarding all other sub-bands which do not effect much the quality (both subjective and objective) (HH2, LH1, HL1 and HH1). Highest CR (191.53), PSNR (78.38 dB), and minimum MSE (0.00094) of still color images are obtained compared to SOFM, EZW and SPIHT