Table 1 - uploaded by Nada A.S. Alwan
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One of the biomedical image problems is the appearance of the bubbles in the slide that could occur when air passes through the slide during the preparation process. These bubbles may complicate the process of analysing the histopathological images. Aims: The objective of this study is to remove the bubble noise from the histopathology images, and...
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Context 1
... way prevents a colour from separating wildly and to achieve a high percentage of correction in finding the desired pixel colour. In this paper, four tests will be carried out, each one has number and type of membership functions (MF) to make a comparative study and find the best way for prediction, Table 1 describes these four tests. According to Figure 6, each test has four inputs and one output (four neighbours and one required pixel respectively) that are called MISO (multi-inputs, single output). ...
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... different histopathological images were executed in the four tests mentioned in Table 1. The way used to find the percentage of correct pixels was measured by taking the red, green and blue values that were produced by the fuzzy controller, and then compared with a reference image (which is the same as input image but before being corrupted by bubble). ...
Context 3
... way prevents a colour from separating wildly and to achieve a high percentage of correction in finding the desired pixel colour. In this paper, four tests will be carried out, each one has number and type of membership functions (MF) to make a comparative study and find the best way for prediction, Table 1 describes these four tests. According to Figure 6, each test has four inputs and one output (four neighbours and one required pixel respectively) that are called MISO (multi-inputs, single output). ...
Context 4
... different histopathological images were executed in the four tests mentioned in Table 1. The way used to find the percentage of correct pixels was measured by taking the red, green and blue values that were produced by the fuzzy controller, and then compared with a reference image (which is the same as input image but before being corrupted by bubble). ...
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Citations
... However, analyzing histopathological images is a challenging task in medical image processing due to the complex appearance, inconsistent staining, variation in illumination, overlapping and clustered nuclei and poorly fixed tissue samples [16,17]. In the tissue preparation procedure, staining can also be affected by various determinants including the tissue itself, the thickness of the tissue section, the length of time at which tissue is exposed to stains, tissue foldings, artifacts in the stains [13], air bubbles [18] and blurring as shown in Fig. 5. All these factors result in poor segmentation and classification in the development of CAD systems. ...
Breast cancer in women is the second most common cancer worldwide. Early detection of breast cancer can reduce the risk of human life. Non-invasive techniques such as mammograms and ultrasound imaging are popularly used to detect the tumour. However, histopathological analysis is necessary to determine the malignancy of the tumour as it analyses the image at the cellular level. Manual analysis of these slides is time consuming, tedious, subjective and are susceptible to human errors. Also, at times the interpretation of these images are inconsistent between laboratories. Hence, a Computer-Aided Diagnostic system that can act as a decision support system is need of the hour. Moreover, recent developments in computational power and memory capacity led to the application of computer tools and medical image processing techniques to process and analyze breast cancer histopathological images. This review paper summarizes various traditional and deep learning based methods developed to analyze breast cancer histopathological images. Initially, the characteristics of breast cancer histopathological images are discussed. A detailed discussion on the various potential regions of interest is presented which is crucial for the development of Computer-Aided Diagnostic systems. We summarize the recent trends and choices made during the selection of medical image processing techniques. Finally, a detailed discussion on the various challenges involved in the analysis of BCHI is presented along with the future scope.
