International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 08 | Aug 2024
p-ISSN: 2395-0072
www.irjet.net
Enhancing Feature Extraction from Histopathology Images of Colorectal Cancer through Comparative Analysis of Pre-processing Filters N. Muhilarasi1, Dr.P.Indra2, Prof.R.Yognapriya3 1PG Scholar., Communication Systems, Department of Electronics and Communication Engineering., Government
College of Engineering Salem , 2Assistant Professor., Department of Electronics and Communication Engineering., Government College of
Engineering Salem , 3Assistant Professor., Department of Electronics and Communication Engineering., Government College of
Engineering Salem, ---------------------------------------------------------------------***--------------------------------------------------------------------those that do can spread to neighboring tissues, Abstract - Colorectal cancer is the second leading particularly the intestinal wall, and eventually metastasize to distant sites via the lymphatic and circulatory systems.
cause of cancer-related deaths worldwide, driven by multiple pathophysiological mechanisms such as abnormal cell proliferation, differentiation, resistance to apoptosis, local invasion by tumor cells, and distant metastasis. This malignancy poses a significant global health challenge, with early detection being paramount to improving patient outcomes. Histopathology images are the primary data source for pathologists, aiding in cancer diagnosis through detailed examination of tissue architecture, cell morphology, and abnormal growth patterns. However, these images often contain artifacts and distortions due to various factors, complicating the extraction of relevant features from colorectal cancer images. In this proposed work, filters are employed for pre-processing, comparing three different types to determine the best method based on Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM) values.
CRC can be analyzed using various methods, including endoscopic, laparoscopic, MRI, CT, and others. Histopathological images (HIs) are based on the morphological and architectural characteristics defined by pathologists and exhibit distinct biological structures. HIs display a high degree of visual diversity in the connected patterns of specific microscopic structures throughout the tissue area. Numerous computerized techniques have been developed to process digitized histology images across the three main computer vision tasks: segmentation, feature extraction, and classification. All these tasks use an image as input. Segmentation extends beyond object detection by identifying specific object pixels rather than using a coarse bounding box. Feature extraction, part of the dimensionality reduction process, involves dividing an initial set of raw data into more manageable groups to simplify processing. Image classification predicts the class or type of an object within the input image, providing a class label as output.
keywords:
Histopathology image analysis, Progressive Switching Median filter, Wiener filter, Gaussian filter.
1.INTRODUCTION
In this proposed work, dataset images undergo preprocessing to achieve high-precision images, enabling pathologists to diagnose patients with high accuracy, distinguishing between malignant and normal tissues.
Colorectal cancer (CRC) is the fourth most common cancer, accounting for 10% of all new cancer cases, and the fifth leading cause of cancer-related deaths, resulting in approximately 550,000 deaths annually worldwide. While 25%-50% of CRC patients are diagnosed at an early stage but later experience recurrence or metastasis, Approximately 25% of colorectal cancer cases are identified at an advanced stage, a condition that is linked to a notably poor prognosis, reflected in a disheartening five-year overall survival rate of merely 14%.. The development of CRC begins with changes in the normal colonic epithelium, including the formation of adenomatous polyps that can grow and multiply over time, accumulating genetic and epigenetic mutations. While not all malignant polyps progress to invasive cancer,
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2. Efficient Preprocessing Filters and Mass Segmentation Techniques for Mammogram Images Authors: Jayesh George M, Perumal Sankar S Affiliation: Department of ECE, Vimal Jyothi Engineering College, Kannur, Kerala .Preprocessing is a crucial step in image processing techniques, aimed at enhancing image quality by reducing unwanted distortions or highlighting specific features essential for further analysis. Mammogram images, in particular, present unique
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