Analysis of different filters for noise reduction in images

Dhruv, Bhawna; Mittal, Neetu; Modi, Megha

doi:10.1109/rdcape.2017.8358306

Cited by 17 publications

(2 citation statements)

References 10 publications

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“…After applying an average filter and an opening morphological transformation with a kernel size of 8 × 8, which are aimed at smoothening the image or reducing the noise caused by the threshold [26,27], the Canny edge detection and the circular Hough transform are used to detect and automatically determine the circle marker position. Once the position is known, the centroid of the circle marker is calculated.…”

Section: Circle and Cross Marker Detectionmentioning

confidence: 99%

Measurement of Si pixel sensor alignment for the ALICE ITS detector

Satya Wicaksana,

Prakasa,

Di Mauro

et al. 2024

Meas. Sci. Technol.

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The ALICE experiment is one of the four experiments at the Large Hadron Collider (LHC) designed to investigate the status of matter under very high energy densities produced during heavy-ion collisions. The ALICE Inner Tracking System (ITS) consists of seven concentric cylindrical layers of monolithic silicon pixel sensors known as ALICE Pixel Detector (ALPIDE). The sensors are used to reconstruct the paths of charged particles generated in the collisions. The sensor alignment of the detector must be adjusted to a high precision standard. The adjustment objective is to obtain a detector that can undertake high-resolution measurements. This paper introduces a method for measuring the reference markers utilized in sensor alignment determination. Markers engraved at the chip corners have been detected using the Hough transform, Canny edge detection, and template matching techniques. The distances between two markers are measured to determine the accuracy of the pixel sensor alignment before and after assembly. The proposed methods exhibit an accuracy exceeding 99% and demonstrate high speed analysis. The average processing times for detecting the circle and cross markers are 105.9 ms/image and 113.8 ms/image, respectively. The sensor alignment of the detector must be adjusted to a high precision standard. However, recent studies have shown deviations of up to 5um above the desired value in the measured sensor position. Such deviations do not represent a major issue, nevertheless it is important to measure them in order to speed-up and make more accurate the recursive track-based alignment procedure used to reconstruct the position of each pixel sensor in the tracking detector. The proposed method offers a promising solution to deliver precise and rapid measurements for a large number of examined objects.

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Section: Circle and Cross Marker Detectionmentioning

confidence: 99%

Measurement of Si pixel sensor alignment for the ALICE ITS detector

Satya Wicaksana,

Prakasa,

Di Mauro

et al. 2024

Meas. Sci. Technol.

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“…The wavelets unlike the conventional Fourier methods do not have smooth base functions. Non-smooth kernel functions of the transform make them effective for abruptly changing signals, such as images affected by noise [27]. The discrete version of the wavelet transform termed as the Discrete Wavelet Transform (DWT) is used as an iterative filer in this work for de-noising images.…”

Section: 1image Pre-processingmentioning

confidence: 99%

Segmentation and classification of brain tumor images using statistical feature extraction and deep neural networks

Joshi¹,

Kumar²,

Chauhan³

2021

IJATEE

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Cancer cases are increasing worldwide, primarily due to unhealthy lifestyles, poor dietary choices, lack of physical activity, increasing pollution levels and exposure to radiation [1]. Early and accurate diagnosis of brain cancers is critical for saving the life of patients. Brain cancer has one of least survival rates for the next 5-10 years from the time of detection of malignant tumours. The cancer progresses from initially being a benign lesion to becoming a cancerous tumor. Cancer causes severe mental trauma, financial strain and social stigma mainly in developing countries.Brain cancer has one of the highest mortalities among all cancers with very low five-year and ten-year survival rates [2]. *Author for correspondenceThe fact is evident from Figure 1 which depicts the Standardized Mortality Ratio (SMR) for different cancer sub-sites. Thus, early detection and correct diagnosis of brain cancer is mandatory for possible chances of recover. This serves as a fundamental motivation for several researchers to develop techniques which could augment early and accurate detection of brain tumors along with a classification of the type of tumor. Early detection is often hindered due to patients overlooking initial symptoms of the disease such as headaches, muscle twitches, occasional unconsciousness and seizures. This causes a delay in the start of diagnosis, which may prove to be lethal owing to the aggressive nature of progress of malignant brain tumors [3]. Typically, the benign tumors exhibit a clear or sharp boundary with respect to the neighboring regions and have a relatively slower spreading rate compared to the malignant tumors. The malignant tumors are much more invasive and exhibit a blurred boundary or distinction and have a much faster spreading rate. The brain

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Image Enhancement Filter

Jha

Singh

2020

Innovations in Computer Science and Engineering

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Analysis of different filters for noise reduction in images

Cited by 17 publications

References 10 publications

Measurement of Si pixel sensor alignment for the ALICE ITS detector

Measurement of Si pixel sensor alignment for the ALICE ITS detector

Segmentation and classification of brain tumor images using statistical feature extraction and deep neural networks

Image Enhancement Filter

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