In this paper, an automatic computer-aided detection system for breast magnetic resonance imaging (MRI) tumour segmentation will be presented. The study is focused on tumour segmentation using the modified automatic seeded region growing algorithm with a variation of the automated initial seed and threshold selection methodologies. Prior to that, some pre-processing methodologies are involved. Breast skin is detected and deleted using the integration of two algorithms, namely the level set active contour and morphological thinning. The system is applied and tested on 40 test images from the RIDER breast MRI dataset, the results are evaluated and presented in comparison to the ground truths of the dataset. The analysis of variance (ANOVA) test shows that there is a statistically significance in the performance compared to the previous segmentation approaches that have been tested on the same dataset where ANOVA p values for the evaluation measures' results are less than 0.05, such as: relative overlap (p =0.0002), misclassification rate (p =0.045), true negative fraction (p =0.0001) and sum of true volume fraction (p =0.0001).
Abstract. In this paper, a segmentation system with a modified automatic Seeded Region Growing (SRG) based on Particle Swarm Optimization (PSO) image clustering will be presented. The paper is focused on Magnetic Resonance Imaging (MRI) breast tumour segmentation. The PSO clusters' intensities are involved in the proposed algorithms of the automated SRG initial seed and threshold value selection. Prior to that, some pre-processing methodologies are involved. And breast skin is detected and deleted using the integration of two algorithms, i.e. Level Set Active Contour and Morphological Thinning. The system is applied and tested on the RIDER breast MRI dataset, and the results are evaluated and presented in comparison to the Ground Truths of the dataset. The results show higher performance compared to the previous segmentation approaches that have been tested on the same dataset.
Problem statement: Data entry form is a convenient and successful tool for information collection by filling in the sheets using pen and handwriting. One of the most important fields in these forms is the data filled boxes. Extracting the handwriting from the data entry forms is important for many purposes such as in documenting and archiving. The extraction process is also important in situations such as when it is necessary to the handwritten recognition process. Approach: A simple and effective approach is presented to extract handwritten characters, including digits and letters of any language from data filled boxes of data entry form and to deal with cases of overlaps between the handwritten characters and boxes lines. The proposed approach is based on line shape characteristic by detecting and removing the vertical and horizontal straight boxes lines, while preserving the curved lines which represent the handwritten characters. The problem of the handwritten characters overlapping with the data filled boxes line is solved using morphology dilation to reconstruct the broken characters after the removal of the boxes lines. Results: Experimental results have demonstrated that the proposed approach can extract handwriting from data filled boxes with overall 94.052% for data collection of 150 forms. Conclusion: The proposed algorithm has been successfully implemented and tested to achieve the objectives of handwritten extraction of any language from data filled boxes. However, this work could not deal with situations whereby the characters touch other immediate characters
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