Conference Papers

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Author: search.filters.author.Patil, N.Subject: search.filters.subject.Image processing

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    Analysis &evaluation of Image filtering Noise reduction technique for Microscopic Images
    (Institute of Electrical and Electronics Engineers Inc., 2020) Devi, T.G.; Patil, N.
    Image processing in the field of microscopy is gaining popularity with the use of advanced techniques used for accurate classification of cells. The abnormalities in the image can be detected accurately after the image has been processed using digital image processing techniques. Preprocessing is an important step in which the noise and other undesirable content will be removed. Preprocessing is essential because the noise will cause inaccuracy in the image processing techniques. Filtering the image to denoise is the first step in preprocessing. The accuracy of denoising using filters determines the quality of the entire image processing cycle. This paper proposes filters to denoise the microscopic images. In this paper, two filters - Wiener and Median filters are compared for accuracy in denoising the image in the preprocessing stage of the cell classification. The Wiener filter and the Median filter were implemented and compared for Peak Signal to Noise Ratio (PSNR) which can be used for better image classification in the later stages. The proposed method was tested using 35 real time images which has Gaussian noise. The two filters perform for the collected dataset whereas the median filter gives highest PSNR outperforming the Wiener filter. © 2020 IEEE.
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    Survey of Leukemia Cancer Cell Detection Using Image Processing
    (Springer Science and Business Media Deutschland GmbH, 2022) Devi, T.G.; Patil, N.; Rai, S.; Philipose, C.S.
    Cancer is the development of abnormal cells that divide at an abnormal pace, uncontrollably. Cancerous cells have the ability to destroy other normal tissues and can spread throughout the body. Cancer cells can develop in various parts of the body. The paper focuses on leukemia which is a type of blood cancer. Blood cancer usually start in the bone marrow where the blood is produced in the body. The types of blood cancer are: Leukemia, Non-Hodgkin lymphoma, Hodgkin lymphoma, and Multiple myeloma. Leukemia is a type of blood cancer that originates in the bone marrow. Leukemia is seen when the body produces an abnormal amount of white blood cells that hinder the bone marrow from creating red blood cells and platelets. Several detection methods to identify the cancerous cells have been proposed. Identification of the cancer cells through cell image processing is very complex. The use of computer aided image processing allows the images to be viewed in 2D and 3D making it easier to identify the cancerous cells. The cells have to undergo segmentation and classification in order to identify the cancerous tumours. Several papers propose segmentation methods, classification methods and some propose both. The purpose of this survey is to review various papers that use either conventional methods or machine learning methods to detect the cells as cancerous and non-cancerous. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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    Soil Type Identification via Deep Learning and Machine Learning Methods
    (Springer Science and Business Media Deutschland GmbH, 2024) Jalapur, S.; Patil, N.
    Soil type identification stands as a crucial concern in numerous countries, to ensure optimal crop yield, farmers need to accurately identify the suitable soil type for specific crops, which plays a significant role in meeting the heightened global food demand. The objective of this survey paper is to present a thorough and up-to-date overview of prevailing methodologies in soil identification, primarily focusing on image analysis, machine learning, and deep learning techniques. The paper initiates by highlighting the significance of soil identification and the limitations inherent in traditional methods. It then delves into the fundamental principles of image processing, deep learning, and spectroscopy, explaining how these techniques can be applied to soil identification. The survey presents an in-depth analysis of various image processing techniques employed for soil identification, including image segmentation, feature extraction, and classification algorithms. Furthermore, it discusses the application of deep learning models for soil classification based on image data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.