Forthcoming articles


International Journal of Image Mining


These articles have been peer-reviewed and accepted for publication in IJIM, 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 Articles will not be published until the final proofs are validated by their authors.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 Image Mining (2 papers in press)


Regular Issues


  • Trigonometry Based Motion Blur Parameter Estimation Algorithm   Order a copy of this article
    by Ruchi Gajjar, Tanish Zaveri, Asim Banerjee, K. V. V. Murthy  
    Abstract: Restoration of blurred images requires information about the blurring function, also known as point spread function (PSF), which is generally unknown in practical applications. Identification of blur parameters is essential for yielding blurring function. This paper proposes a technique for estimation of parameters of motion blur by formulating the trigonometric relationship between the spectral lines of the motion blurred image and the blur parameters. In majority of the existing motion blur parameter estimation approaches, the length is estimated by rotating the spectrum to the estimated angle. This requires the angle estimation to be done forehand. The proposed method estimates both, length and angle simultaneously by deriving the trigonometric relation between spectral lines, thereby eliminating the need of rotating the spectrum for length estimation. The proposed technique is applied on standard database Berkeley segmentation dataset, Pascal VOC 2007 dataset and USC-SIPI Image database. The simulation results show that length and angle are accurately estimated by the proposed method. The performance of proposed method is compared with existing state of art techniques which show that proposed method exhibit better performance in terms of the test range and parameter estimation.
    Keywords: image degradation; motion blur; parameter estimation; point spread function.

Special Issue on: ICIA-16 Image Processing and Analysis

  • Analysis of Diverse Optimisation Algorithms in Breast Cancer Detection
    by Senthil Kumar K, Venkata lakshmi K, Karthikeyan K, JasiyaJabeen A 
    Abstract: Breast cancer is a widespread problem faced by the women in recent years. It is highly essential to detect the breast cancer at an early stage to save lives. The ultrasound image helps more in the diagnosis and analysis of breast cancer than the mammogram. Image segmentation technique is used to segment the mistrustful masses from an ultrasound image of the breast. Optimization algorithms play a vital role in image segmentation techniques which increases the efficiency and accuracy of image segmentation results. This work focuses on implementation and analysis of various optimization algorithms in detecting mistrustful masses in the given ultrasound image of the breast. In preprocessing the speckle noise is reduced by using the median filter and Gaussian filter and proved that the median filter has better performance than the Gaussian filter in the isolation of speckle noise. The visual appearance of the input image is improved by using adaptive histogram equalization. The preprocessed input image is subject to image segmentation techniques with various optimization algorithms viz. particle swarm optimization, chaotic particle swarm optimization, k-Medoid clustering, fuzzy c Means and k-Means clustering with manual selection of cluster centers. A comparative analysis has been done on the above said algorithms using MATLAB and from the results, it is proved that the chaotic particle swarm optimization algorithm has best result among the others. The accuracy and dice similarity coefficient of the chaotic particle swarm optimisation based method is 93.5793 and 0.8735 respectively. This proves that the chaotic particle swarm optimization algorithm is highly suitable in segmenting the breast ultrasound image.
    Keywords: Ultrasound image, Median filter, Gaussian filter, Histogram Equalisation, Particle swarm optimization, Chaotic particle swarm optimization, k-Medoids, Fuzzy c-Means, k-Means clustering and Dice coefficient.