Effect of Different Filtering Techniques on Medical and Document Image

Mustafa, Wan Azani; Sam, Syafiq; Jamlos, Mohd Aminudin; Khairunizam, Wan

doi:10.1007/978-981-15-5281-6_52

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…As shown in Figure 3 a, most Pap smear images were low-contrast and noisy. As a result, image processing was required to reduce noise and raise contrast [ 30 ]. To eliminate the noise, a median filter was utilised.…”

Section: Methodsmentioning

confidence: 99%

Cervical Net: A Novel Cervical Cancer Classification Using Feature Fusion

et al. 2022

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Cervical cancer, a common chronic disease, is one of the most prevalent and curable cancers among women. Pap smear images are a popular technique for screening cervical cancer. This study proposes a computer-aided diagnosis for cervical cancer utilising the novel Cervical Net deep learning (DL) structures and feature fusion with Shuffle Net structural features. Image acquisition and enhancement, feature extraction and selection, as well as classification are the main steps in our cervical cancer screening system. Automated features are extracted using pre-trained convolutional neural networks (CNN) fused with a novel Cervical Net structure in which 544 resultant features are obtained. To minimise dimensionality and select the most important features, principal component analysis (PCA) is used as well as canonical correlation analysis (CCA) to obtain the best discriminant features for five classes of Pap smear images. Here, five different machine learning (ML) algorithms are fed into these features. The proposed strategy achieved the best accuracy ever obtained using a support vector machine (SVM), in which in which fused features between Cervical Net and Shuffle Net is 99.1% for all classes.

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Section: Methodsmentioning

confidence: 99%

Cervical Net: A Novel Cervical Cancer Classification Using Feature Fusion

et al. 2022

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“…It helps in solving problems of the image display [10] and, additionally, enables the improvement of image quality. The problems that always happened in images are illumination, noise, and under-light images [20]. Hence, one of the challenges is to remove noise from images [16,32].…”

Section: Introductionmentioning

confidence: 99%

A Power-Efficient Multichannel Low-Pass Filter Based on the Cascaded Multiple Accumulate Finite Impulse Response (CMFIR) Structure for Digital Image Processing

Jain¹,

Chakrabarti²,

Mitolo

et al. 2022

Circuits Syst Signal Process

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The author offers a power-efficient multichannel low-pass filter for digital image processing based on the cascade multiple accumulate finite impulse response (CMFIR) structure in this study. The CMFIR filter was created using the outputs of a linear time-invariant system (LTI), which was built using a cascaded integrator comb (CIC) and a MAC low-pass filter. The sample rate convertor based on CIC filters effectively conducts decimation or interpolation. The sample rate convertor with the CIC filter can only accommodate narrowband transmissions and so cannot be utilized for wideband signals. The MAC architecture-based sample rate convertor is a good solution for high-bandwidth signals, but it uses more resources like registers and flip-flops, which increases power consumption. Here, the CMFIR low-pass filter acts as an interpolator, introducing a sample to boost the image's resolution. CMFIR is a useful tool for addressing the issue of aliasing during sampling. In addition, the genetic algorithm was used to increase the filter's resource utilization and power consumption efficiency.

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Document Image Quality Assessment: A Survey

et al. 2023

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The rapid emergence of new portable capturing technologies has significantly increased the number and diversity of document images acquired for business and personal applications. The performance of document image processing systems and applications depends directly on the quality of the document images captured. Therefore, estimating the document's image quality is an essential step in the early stages of the document analysis pipeline. This paper surveys research on Document Image Quality Assessment (DIQA). We first provide a detailed analysis of both subjective and objective DIQA methods. Subjective methods, including ratings and pair-wise comparison-based approaches, are based on human opinions. Objective methods are based on quantitative measurements, including document modeling and human perception-based methods. Second, we summarize the types and sources of document degradations and techniques used to model degradations. In addition, we thoroughly review two standard measures to characterize document image quality: Optical Character Recognition (OCR)-based and objective human perception-based. Finally, we outline open challenges regarding developing DIQA methods and provide insightful discussion and future research directions for this problem. This survey will become an essential resource for the document analysis research community and serve as a basis for future research.

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Effect of Different Filtering Techniques on Medical and Document Image

Cited by 3 publications

References 19 publications

Cervical Net: A Novel Cervical Cancer Classification Using Feature Fusion

Cervical Net: A Novel Cervical Cancer Classification Using Feature Fusion

A Power-Efficient Multichannel Low-Pass Filter Based on the Cascaded Multiple Accumulate Finite Impulse Response (CMFIR) Structure for Digital Image Processing

Document Image Quality Assessment: A Survey

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