A software tool for automatic classification and segmentation of 2D/3D medical images

Strzelecki, Michał; Szczypiński, Piotr M.; Materka, Andrzej; Klepaczko, Artur

doi:10.1016/j.nima.2012.09.006

Cited by 213 publications

(136 citation statements)

References 14 publications

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“…Histogram analyses were performed using MaZda (MaZda for Windows, B11 version 3.3, www. eletel.p.lodz.pl/programy/mazda/) (28,29). Two abdominal radiologists, with one year (Y.Z.X.)…”

Section: Histogram Analysismentioning

confidence: 99%

Feasibility of histogram analysis of susceptibility-weighted MRI for staging of liver fibrosis

Yang¹,

Liang²,

Hu³

et al. 2016

Diagn Interv Radiol

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“…Histogram analyses were performed using MaZda (MaZda for Windows, B11 version 3.3, www. eletel.p.lodz.pl/programy/mazda/) (28,29). Two abdominal radiologists, with one year (Y.Z.X.)…”

Section: Histogram Analysismentioning

confidence: 99%

Feasibility of histogram analysis of susceptibility-weighted MRI for staging of liver fibrosis

Yang¹,

Liang²,

Hu³

et al. 2016

Diagn Interv Radiol

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“…In addition, the K-NN classifier implemented in the MaZda program uses the leave one out testing technique, which does not require a separate training data set [9]. The experimental results are illustrated in Table 4.…”

Section: Discussionmentioning

confidence: 99%

Textural Analysis of Liver Focal Lesions with Co-occurrence Matrix and Wavelet Transform on CT: A Feasible Study in FNH, HEM and HCC

Qiu¹,

Wu²,

Hui³

et al. 2017

dtetr

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Abstract. Focal nodular hyperplasia (FNH), hepatocellular carcinoma (HCC) and cavernous hemangioma (HEM) are three types of solid focal liver lesions. Using CT images to identify these three types of lesions is the most commonly method. However, this method usually mainly depends on experiences. Whereas, more objective and quantitative image information could be explored with texture analysis method. This research aims to discuss the appreciations of texture analysis based on CT images for identifying FNH, HCC and HEM. This paper retrospectively analyzed 81 clinically or pathologically diagnosed cases, each of which contains contrast non-enhanced and contrast two-phasic enhanced CT images. The texture analysis was based on gray level co-occurrence matrix (GLCM) and wavelet transform. The results shows that the misclassification rates of texture classification were low to 2.73% between FNH and HEM (between benign lesions), 3.19% between FNH and HCC (between benign lesions and malignant lesions), and 1.67% between HEM and HCC (between benign lesions and malignant lesions) respectively. The effect of texture classification based on contrast two-phasic enhanced CT images was better than contrast non-enhanced CT images.

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“…The normalized energy of the image at each scale is typically computed to provide the parameters of the image texture. The MaZda software [24,26] computes Haar-wavelet DWT up to 8 scales with a cascade of high and low-pass filters, resulting in four subbands (low-low, low-high, high-low and high-high) for each scale. This is also reflected in names used for estimated energies of wavelet coefficients.…”

Section: Wavelet Coefficientsmentioning

confidence: 99%

On The Effect Of Image Brightness And Contrast Nonuniformity On Statistical Texture Parameters

Materka

Strzelecki

2015

Foundations of Computing and Decision Sciences

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Abstract. Computerized texture analysis characterizes spatial patterns of image intensity, which originate in the structure of tissues. However, a number of texture descriptors also depend on local average image intensity and/or contrast. This variations, known as image nonuniformity (inhomogeneity) artefacts often occur, e.g. in MRI. Their presence may lead to errors in tissue description. This unwanted effect is explained in this paper using statistical texture descriptors applied for MRI slices of a normal and fibrotic liver. To reduce the errors, correction of image spatial nonuniformity prior to texture analysis is performed. The issue of sensitivity of popular texture parameters to image nonuniformities is discussed. It is illustrated by classification examples of natural Brodatz textures, digitally modified to account for inhomogeneities -modeled as smooth variations of image intensity and contrast. A set of texture features is identified which represent certain immunity to image inhomogeneities.

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A software tool for automatic classification and segmentation of 2D/3D medical images

Cited by 213 publications

References 14 publications

Feasibility of histogram analysis of susceptibility-weighted MRI for staging of liver fibrosis

Feasibility of histogram analysis of susceptibility-weighted MRI for staging of liver fibrosis

Textural Analysis of Liver Focal Lesions with Co-occurrence Matrix and Wavelet Transform on CT: A Feasible Study in FNH, HEM and HCC

On The Effect Of Image Brightness And Contrast Nonuniformity On Statistical Texture Parameters

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