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This paper introduces a novel approach in image processing based on a vector image model. A major advantage of the model is that it allows vector operations to be performed on an image. An example of a vector operation is the computation of mechanical moments for detecting inhomogeneities in an object or equivalently edges in an image. A new edge operator derived from a vector image model yields an edge vector field analogous to the Hamiltonian gradient field of the image. The distinct feature of the edge vector field is that edge vectors form current loops encompassing the objects. This feature is exploited to develop a new boundary extraction algorithm based on particle motion in a force field. The edge vector field forces a particle to move along the edges while an orthogonal normalized Laplacian gradient vector field guarantees that the particle will not drift away from the edges. The object boundary can be obtained from the convergent path of the particle trajectory. Using a fine stepping factor, the extracted boundary can achieve subpixel accuracy. The proposed algorithm has major advantages over the conventional edge-detection, edge-thinning, and edge-linking techniques in that it effectively utilizes both direction and magnitude of edges. The algorithm is simple, robust and performs very well even on high curvature objects.

Objective The aim of this study was to develop automated software for screening and diagnosing diabetic retinopathy (DR) from fundus photograph of patients with diabetes mellitus. Methods The extraction of clinically significant features to detect pathologies of DR and the severity classification were performed by using MATLAB R2015a with MATLAB Image Processing Toolbox. In addition, the graphic user interface was developed using the MATLAB GUI Toolbox. The accuracy of software was measured by comparing the obtained results to those of the diagnosis by the ophthalmologist. Results A set of 400 fundus images, containing 21 normal fundus images and 379 DR fundus images (162 non-proliferative DR and 217 proliferative DR), was interpreted by the ophthalmologist as a reference standard. The initial result showed that the sensitivity, specificity and accuracy of this software in detection of DR were 98%, 67% and 96.25%, respectively. However, the accuracy of this software in classifying non-proliferative and proliferative diabetic retinopathy was 66.58%. The average time for processing is 7 seconds for one fundus image. Conclusion The automated DR screening software was developed by using MATLAB programming and yielded 96.25% accuracy for the detection of DR when compared to that of the diagnosis by the ophthalmologist. It may be a helpful tool for DR screening in the distant rural area where ophthalmologist is not available.

This study addresses the problem of locating sugar cane loading stations and allocating cane fields to those stations. The problem is different from the general location–allocation problem in so far as this framework takes into account the different maturity periods of each cane field. If the loading station is improperly located, it can result in high transportation costs from cane fields to the station and significant fluctuations in the station utilization rate. A modification of the well-known "fuzzy c-means" (FCM) method, which takes into account both the cane supply and the different cane maturity periods, is proposed to solve this problem. The objective of the model is to minimize the sum of the transportation and station utilization costs. The performance of this method is compared to that of the traditional FCM method. The results show that the proposed approach is practical for solving the problem and that it provides a better solution than the FCM method.