Forthcoming and Online First Articles

International Journal of Signal and Imaging Systems Engineering

International Journal of Signal and Imaging Systems Engineering (IJSISE)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Signal and Imaging Systems Engineering (2 papers in press)

Regular Issues

  • Syntactic approach to reconstruct simple and complex medical images   Order a copy of this article
    by Shilpa Rani, Kamlesh Lakhwani, Sandeep Kumar 
    Abstract: Pattern recognition is always a fascinating area of research for most of the researchers. A person can easily recognize the objects which are different in shape, size, color, and scale. Most of the available model uses a statistical approach for object recognition which is a good choice if noise is present in the image and images are simple but this method fails if patterns are more complex and there is a possibility of ambiguous results for complex pattern datasets. In that case, structural pattern recognition is more helpful. We focused on the syntactic approach for describing the features as knowledge and this technique represents the image in textual form using a syntactic approach which could be a great contribution in the field of theoretical computer science. Representation of image or object in textual form has been done through picture description language (PDL). There is a possibility that objects could be scattered in the image or maybe many objects are present in the same image. To identify the feature vector of all the objects, the gap-filling algorithm is applied which is a novel approach of the proposed method. The obtained feature vector can be used for the reconstruction of the original image. Experiments have performed on Brain MRI datasets and own dataset and the algorithm are able to convert a simple or complex image in textual form and reconstruction of an image using a knowledge vector is also done. To identify the performance of the reconstruction algorithm MAE, CPU time, and RMSE and iteration of the frame are calculated. MAE is 0.125 and 0.127 on its own and brain MRI dataset.CPU time is 1ms and 10ms on own and brain MRI dataset. Iteration of the frame is 2272it/s and 96it/s on own and brain MRI dataset. The performance of the algorithm is better than the existing techniques.GUI of the proposed work is also created for the better experience of user.
    Keywords: Pattern recognition; structural pattern recognition; PDL.

  • Segmentation and detection of the retinal vascular network using fast filtering   Order a copy of this article
    by Nabila Rahmoune, Adel Rahmoune 
    Abstract: Changes in retinal blood vessels are a characteristic sign of many retinal diseases. Therefore, the automatic segmentation of vessels is an essential element for the diagnosis of different ocular diseases. In this paper, we present a novel algorithm for the detection and the segmentation of the vascular network of blood vessels in fundus images. Our algorithm employs two mean linear filters using the convolutional kernel, one directional along a line and the second on a square region, in combination with thresholding. The proposed approachs performance was tested on the public datasets DRIVE and STARE. Based on the test results, the mean segmentation accuracy, sensitivity, specificity and time complexity of retinal images in DRIVE are 94.27%, 97.01%, 66.20% and 1.63s and for the STARE database, they are 93.41%, 95.54%, 66.55% and 2.13s respectively. The proposed algorithm is simple and very fast. It achieved satisfactory mean segmentation accuracy with very low time complexity.
    Keywords: retinal blood vessel; image segmentation; mean linear filter; retinopathy.