Analysis &amp; Evaluation of Image filtering Noise reduction technique for Microscopic Images

Devi, Tulasi Gayatri; Patil, Nagamma

doi:10.1109/icitiit49094.2020.9071556

Cited by 12 publications

(3 citation statements)

References 24 publications

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“…This section of the paper analyzes different image processing techniques that have been used to detect cancer cells. In [ 11 ], a color thresholding algorithm-based Acute Lymphoblastic Leukemia (ALL) detection system is proposed, that firstly detects the white blood cells, segments the lymphocytes among the detected white blood cells, and then finally identifies and counts the lymphoblasts. An accuracy of 92.15%, sensitivity of 96.92%, and precision of 91.35%, were found for a sample size of 260 patient images.…”

Section: Methodsmentioning

confidence: 99%

Gaussian Blurring Technique for Detecting and Classifying Acute Lymphoblastic Leukemia Cancer Cells from Microscopic Biopsy Images

Devi

Patil

Rai

et al. 2023

Life

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Visual inspection of peripheral blood samples is a critical step in the leukemia diagnostic process. Automated solutions based on artificial vision approaches can accelerate this procedure, while also improving accuracy and uniformity of response in telemedicine applications. In this study, we propose a novel GBHSV-Leuk method to segment and classify Acute Lymphoblastic Leukemia (ALL) cancer cells. GBHSV-Leuk is a two staged process. The first stage involves pre-processing, which uses the Gaussian Blurring (GB) technique to blur the noise and reflections in the image. The second stage involves segmentation using the Hue Saturation Value (HSV) technique and morphological operations to differentiate between the foreground and background colors, which improve the accuracy of prediction. The proposed method attains 96.30% accuracy when applied on the private dataset, and 95.41% accuracy when applied on the ALL-IDB1 public dataset. This work would facilitate early detection of ALL cancer.

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Section: Methodsmentioning

confidence: 99%

Gaussian Blurring Technique for Detecting and Classifying Acute Lymphoblastic Leukemia Cancer Cells from Microscopic Biopsy Images

Devi

Patil

Rai

et al. 2023

Life

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“…The enhance images is fed in to Laplacian filter to display the edges of WBC cell and improve the low contrast among the WBC cells and cytoplasm. Devi T G and Patil N [9] implemented the two filters Weiner and Median filter for the comparison of accuracy in denoising the images using the PSNR value for the better classification of cells. They have found the Median filter is 8% better than the Weiner filter.…”

Section: Literature Reviewmentioning

confidence: 99%

The evaluation of denoising techniques on microscopic blood smear images of Acute Lymphoblastic Leukemia (ALL)

Jayesh Gangadhar Shinde,

Srivaramangai R

2023

Open Access Res. J. Eng. Technol.

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Preprocessing is the first step in the image processing for any digital image before it goes further step. Denoising techniques are one of the important techniques used in preprocessing. The digitized microscopic blood smear image contains unwanted noise due to poor illumination, electronic interference, different variation in lighting condition etc. The processing of these images without the filtering techniques, can produce inaccurate results in the further step of image processing such as segmentation, feature extraction and classification. So, it is necessary to preprocess the images with proper techniques for each type of preprocessing. In this paper, we have tried to evaluate the different types of the filtering techniques on the blood smear images of Acute Lymphoblastic Leukemia (ALL) for the removal of noise. We have reviewed many research work which have used various filtering techniques used for removal of noise.

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“…evaluating the performance of filters in literature, the most commonly used metrics are RMSE and Signal to Noise Ratio (SNR); the latter mostly used when removing noise in images(Zhang et al (2012),Muppalla et al (2018),Devi and Patil (2020) andBi et al (2021)…”

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confidence: 99%

Low-pass filter application for noise removal in water quality data with high temporal resolution

Coraggio,

Gronow,

Tryfonas

et al. 2023

Preprint

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Large datasets with high temporal resolution are becoming widely available through the use of wireless sensors and other low-effort, automated data collection techniques. The higher the sampling frequency is, the more obvious and significant the noise will be due to the highly unrealistic oscillations in the observations. Machine learning techniques work well with large amounts of data, but it is essential to ensure that the data collected is as clean as possible from noise; otherwise, the machine learning algorithm will struggle to predict the actual data and instead attempt to reproduce the noise. This study explores the use of four low pass filters: Butterworth, Chebyshev I, Chebyshev II and Savitzky-Golay filter for removing noise from water quality dataset with high temporal resolution. This study describes how the filters are implemented and gives advice on how to evaluate the filters’ capability to reduce noise and preserve signal features. The method is applied to five water quality parameters based on a water quality dataset with a 5 minutes resolution collected in an urban surface water body in Bristol, United Kingdom. Based on the results of this study, it has been found that for the analysed water quality parameters (conductivity, water temperature, dissolved oxygen, and fDOM) Butterworth filter with a cut-off frequency between 2.33E-05 Hz~12 hours and 4.5E-04 Hz~ 6 hours is the filter that allows the best compromise between noise removal and signal preservation

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Analysis & Evaluation of Image filtering Noise reduction technique for Microscopic Images

Cited by 12 publications

References 24 publications

Gaussian Blurring Technique for Detecting and Classifying Acute Lymphoblastic Leukemia Cancer Cells from Microscopic Biopsy Images

Gaussian Blurring Technique for Detecting and Classifying Acute Lymphoblastic Leukemia Cancer Cells from Microscopic Biopsy Images

The evaluation of denoising techniques on microscopic blood smear images of Acute Lymphoblastic Leukemia (ALL)

Low-pass filter application for noise removal in water quality data with high temporal resolution

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