&lt;title&gt;Computer-assisted diagnosis of chest radiographs for pneumoconioses&lt;/title&gt;

Solíz, Peter; Pattichis, Marios S.; Ramachandran, Janakiramanan; James, David S.

doi:10.1117/12.431143

Cited by 8 publications

(8 citation statements)

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“…The authors plan as future work the testing of the system using a much bigger database. The authors produced classification results that were significantly better than those presented in [102,103,107].…”

Section: Pneumoconiosis In Chest Radiographsmentioning

confidence: 88%

“…An AM-FM method for grading chest radiographs according to the ILO standards is presented in [67,68] by Murray et al Related work, by different research groups, can be found in [102][103][104][105][106][107][108]. First, a logarithmic transformation to the images is applied to improve the contrast of the X-Rays [109].…”

Section: Pneumoconiosis In Chest Radiographsmentioning

confidence: 99%

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Recent multiscale AM-FM methods in emerging applications in medical imaging

Murray

Pattichis

Barriga

et al. 2012

EURASIP J. Adv. Signal Process.

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Amplitude-modulation frequency-modulation (AM-FM) decompositions represent images using spatially-varying sinusoidal waves and their spatially-varying amplitudes. AM-FM decompositions use different scales and bandpass filters to extract the wide range of instantaneous frequencies and instantaneous amplitude components that may be present in an image. In the past few years, as the understanding of its theory advanced, AM-FM decompositions have been applied in a series of medical imaging problems ranging from ultrasound to retinal image analysis, yielding excellent results. This article summarizes the theory of AM-FM decompositions and related medical imaging applications.

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Section: Pneumoconiosis In Chest Radiographsmentioning

confidence: 88%

Section: Pneumoconiosis In Chest Radiographsmentioning

confidence: 99%

Recent multiscale AM-FM methods in emerging applications in medical imaging

Murray

Pattichis

Barriga

et al. 2012

EURASIP J. Adv. Signal Process.

Self Cite

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show abstract

“…The study found five major categories of texture feature analysis methods, where texture feature was extracted using Fourier spectrum [ 39 , 40 , 44 , 48 , 50 , 53 , 58 ], co-occurrence matrix analysis [ 42 , 48 , 50 , 53 , 55 , 57 , 58 , 59 , 61 , 64 , 79 ], histogram analysis [ 34 , 47 , 50 , 55 , 59 , 61 , 63 , 66 ], wavelet transform [ 52 , 56 ], and density distribution [ 42 , 45 , 46 , 51 , 54 , 60 , 62 ]. The details of the five methods are discussed in the following subsections and the texture features extracted from them, as described in Table 1 , are summarised.…”

Section: Analysis Of Returned Articlesmentioning

confidence: 99%

“…The tonal distribution indicates the variation of colours, especially grey-level intensity distribution, measured from histogram analysis [ 86 , 87 ]. In CAD of CWP diseases, several researchers have proposed extracting texture features using histogram analysis [ 34 , 47 , 50 , 55 , 59 , 61 , 63 , 66 ]. Most of them were computed using a set of common features, namely, mean, variance, skewness, kurtosis, energy, and entropy, from the grey-level intensity distribution of ROIs images.…”

Section: Analysis Of Returned Articlesmentioning

confidence: 99%

Computer-Aided Diagnosis of Coal Workers’ Pneumoconiosis in Chest X-ray Radiographs Using Machine Learning: A Systematic Literature Review

Devnath

Summons

Luo

et al. 2022

IJERPH

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Computer-aided diagnostic (CAD) systems can assist radiologists in detecting coal workers’ pneumoconiosis (CWP) in their chest X-rays. Early diagnosis of the CWP can significantly improve workers’ survival rate. The development of the CAD systems will reduce risk in the workplace and improve the quality of chest screening for CWP diseases. This systematic literature review (SLR) amis to categorise and summarise the feature extraction and detection approaches of computer-based analysis in CWP using chest X-ray radiographs (CXR). We conducted the SLR method through 11 databases that focus on science, engineering, medicine, health, and clinical studies. The proposed SLR identified and compared 40 articles from the last 5 decades, covering three main categories of computer-based CWP detection: classical handcrafted features-based image analysis, traditional machine learning, and deep learning-based methods. Limitations of this review and future improvement of the review are also discussed.

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“…Classification is based on contrast features computed from region grown areas. Recently, Soliz et al [182] presented a system for the detection of pneumoconiosis using a specific type of neural network and features derived from cooccurrence matrices from manually selected ROIs. To our knowledge, none of the work on detecting pneumoconiosis has been used in practice or evaluated for clinical use.…”

Section: Texture Analysismentioning

confidence: 99%