International Journal of Signal and Imaging Systems Engineering 

About: International Journal of Signal and Imaging Systems Engineering is an academic journal published by Inderscience Publishers. The journal publishes majorly in the area(s): Image segmentation & Image compression. It has an ISSN identifier of 1748-0698. It is also open access. Over the lifetime, 266 publications have been published receiving 1352 citations. The journal is also known as: Int. j. signal and imaging systems engineering & IJSISE.

X-ray imaging for homeland security

Abstract: Since 9/11, there has been a growing interest to improve the security both of air travel and of cargo shipping. Visual inspection is the simplest tool and is very efficient when single objects of small size are to be checked. The complexity of inspection increases when we have large 'boxes' and/or when several objects are packed into a closed space, as in luggage or even in larger volumes like shipping containers or trucks. This paper gives a brief overview of how different X-ray techniques are used for fast checking of small and large objects.

An effective image processing method for detection of diabetic retinopathy diseases from retinal fundus images

Abstract: Diabetic retinopathy (i.e., DR), is an eye disorder caused by diabetes, diabetic retinopathy detection is an important task in retinal fundus images due the early detection and treatment can potentially reduce the risk of blindness. Retinal fundus images play an important role in diabetic retinopathy through disease diagnosis, disease recognition (i.e., by ophthalmologists), and treatment. The current state-of-the-art techniques are not satisfied with sensitivity and specificity. In fact, there are still other issues to be resolved in state-of-the-art techniques such as performances, accuracy, and easily identify the DR disease effectively. Therefore, this paper proposes an effective image processing method for detection of diabetic retinopathy diseases from retinal fundus images that will satisfy the performance metrics (i.e., sensitivity, specificity, accuracy). The proposed automatic screening system for diabetic retinopathy was conducted in several steps: Pre-processing, optic disc detection and removal, blood vessel segmentation and removal, elimination of fovea, feature extraction (i.e., Micro-aneurysm, retinal hemorrhage, and exudates), feature selection and classification. Finally, a software-based simulation using MATLAB was performed using DIARETDB1 dataset and the obtained results are validated by comparing with expert ophthalmologists. The results of the conducted experiments showed an efficient and effective in sensitivity, specificity and accuracy.

A correlogram algorithm for image indexing and retrieval using wavelet and Rotated Wavelet Filters

Abstract: In this paper, a new image-indexing algorithm with the combination of wavelet and Rotated Wavelet Correlogram (RWC) is proposed in contrast to the Gabor Wavelet Correlogram (GWC) The 0° and 90° information (sub-band) of image collected from Standard Wavelet Filters (SWFs) and +45° and −45° are collected from Rotated Wavelet Filters (RWFs) and further used for correlogram feature calculation The data handling with proposed method is very less compared with GWC therefore this method is six times faster than the GWC, which make it suitable for online application The retrieval results of the proposed method are tested on three different databases, ie, Corel 1000 (DB1) natural image database, MIT VisTex (DB2) texture image database and Corel 2450 (DB3) natural image database The results after being investigated show a significant improvement in terms of their evaluation measures as compared with most of existing techniques on respective databases

Echocardiography image segmentation using feed forward artificial neural network (FFANN) with fuzzy multi-scale edge detection (FMED)

Abstract: In the recent past Echocardiography image segmentation is one of the significant process describes about the segment out inner and outer walls or other parts of the organ boundaries. However, this kind of segmentation process is one of the difficult for physicians because of inexperience or subject specialists with the previous cases. To enhance the cardiac image segmentation accuracy and to minimise the segmentation time a machine learning method such as neural networks has been proposed in the segmentation process. In this research, feed forward artificial neural network (FFANN) has been utilised and fuzzy multi-scale edge detection (FMED) process has been applied to detect the segmented edges to define the detected texture boundary with the help of FFANN weights. An experimental result shows an efficient learning capacity of FFANN and this work deals with the segmentation of ultrasound images using MATLAB implementation.

Toward a more complete electrodynamic theory

Abstract: Maxwell’s equations require a gauge condition for specific solutions. This incompleteness motivates use of a dynamical quantity,