Diabetic retinopathy (DR) is increasing progressively pushing the demand of automatic extraction and classification of severity of diseases. Blood vessel extraction from the fundus image is a vital and challenging task. Therefore, this paper presents a new, computationally simple, and automatic method to extract the retinal blood vessel. The proposed method comprises several basic image processing techniques, namely edge enhancement by standard template, noise removal, thresholding, morphological operation, and object classification. The proposed method has been tested on a set of retinal images. The retinal images were collected from the DRIVE database and we have employed robust performance analysis to evaluate the accuracy. The results obtained from this study reveal that the proposed method offers an average accuracy of about 97 %, sensitivity of 99 %, specificity of 86 %, and predictive value of 98 %, which is superior to various well-known techniques.
This study was focused on investigating the optimum fluidising velocity during the combustion of rice husk in a bench-scale fluidised bed combustor (ID 210mm) to obtain low carbon ash in the amorphous form. When all other parameters are held constant, the optimum fluidizing velocity aids in almost complete combustion, thereby releasing the entrapped carbon for further conversion. This results in ash with consistently low carbon content (less than 2wt%). The range of fluidising velocities investigated was from as low as 1.5U(mf) to as high as 8U(mf). It was found that the optimum fluidising velocity was approximately 3.3U(mf) as the mixing of rice husk with the bed was good with a high degree of penetration into the sand bed. The resulting ash retained its amorphous form with low residual carbon content (at 2.88wt%) and minimal sand contamination as shown by the X-ray diffraction analysis.