On the Influence of Image Features Wordlength Reduction on Texture Classification

Strzelecki, Michał; Kociołek, Marcin; Materka, Andrzej

doi:10.1007/978-3-319-91211-0_2

Cited by 9 publications

(4 citation statements)

References 17 publications

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“…The "noise" removal reduced classification uncertainty of discriminating cocoa agroforests from other vegetation cover types. Similarly, application of grey level smoothing of medical images, natural and Magnetic Resonance (MR) images, improved classification accuracy while reducing the computational costs [68].…”

Section: Discussionmentioning

confidence: 99%

Delineation of Cocoa Agroforests Using Multiseason Sentinel-1 SAR Images: A Low Grey Level Range Reduces Uncertainties in GLCM Texture-Based Mapping

Numbisi

Coillie

Wulf

2019

IJGI

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Delineating the cropping area of cocoa agroforests is a major challenge in quantifying the contribution of land use expansion to tropical deforestation. Discriminating cocoa agroforests from tropical transition forests using multispectral optical images is difficult due to the similarity of the spectral characteristics of their canopies. Moreover, the frequent cloud cover in the tropics greatly impedes optical sensors. This study evaluated the potential of multiseason Sentinel-1 C-band synthetic aperture radar (SAR) imagery to discriminate cocoa agroforests from transition forests in a heterogeneous landscape in central Cameroon. We used an ensemble classifier, Random Forest (RF), to average the SAR image texture features of a grey level co-occurrence matrix (GLCM) across seasons. We then compared the classification performance with results from RapidEye optical data. Moreover, we assessed the performance of GLCM texture feature extraction at four different grey levels of quantization: 32 bits, 8 bits, 6 bits, and 4 bits. The classification’s overall accuracy (OA) from texture-based maps outperformed that from an optical image. The highest OA (88.8%) was recorded at the 6 bits grey level. This quantization level, in comparison to the initial 32 bits in the SAR images, reduced the class prediction error by 2.9%. The texture-based classification achieved an acceptable accuracy and revealed that cocoa agroforests have considerably fragmented the remnant transition forest patches. The Shannon entropy (H) or uncertainty provided a reliable validation of the class predictions and enabled inferences about discriminating inherently heterogeneous vegetation categories.

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Section: Discussionmentioning

confidence: 99%

Delineation of Cocoa Agroforests Using Multiseason Sentinel-1 SAR Images: A Low Grey Level Range Reduces Uncertainties in GLCM Texture-Based Mapping

Numbisi

Coillie

Wulf

2019

IJGI

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“…Nevertheless, the processed data have a 12-bit precision; therefore, more information can be obtained using the entire scale. In previous studies, various methods were proposed to reduce the bit resolution and related noise level [50,51]; however, the authors also considered the loss of information that occurred during the image resolution reduction from 12 to 8 bits and decided to examine whether an appropriate preprocessing method might improve the applicability of the proposed texture operator. Therefore, several transformations that highlighted certain ranges were considered.…”

Section: Preprocessingmentioning

confidence: 99%

Caries detection enhancement using texture feature maps of intraoral radiographs

et al. 2018

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Objectives Dental caries are caused by tooth demineralization due to bacterial plaque formation. However, the resulting lesions are often discrete and thus barely recognizable in intraoral radiography images. Therefore, more advanced detection techniques are in great demand among dentists and radiographers. This study was performed to evaluate the performance of texture feature maps in the recognition of discrete demineralization related to caries plaque formation. Methods Digital intraoral radiology image analysis protocols incorporating first-order features (FOF), co-occurrence matrices, gray tone difference matrices, run-length matrices (RLM), local binary patterns (LBP), and k-means clustering (CLU) were used to transform the digital intraoral radiology images of 10 patients with confirmed caries, which were retrospectively reviewed in a dental clinic. The performance of the resulting texture feature maps was compared with that of radiographic images by radiologists and dental specialists. Results Significantly improved detection of caries spots was achieved by employing the CLU and FOF texture feature maps. The caries-affected area with sharp margins was well defined using the CLU approach. A pseudo-three-dimensional effect was observed in outlining the demineralization zones inside the cavity with the FOF 5 protocol. In contrast, the LBP and RLM techniques produced less satisfactory results with unsharp edges and less detailed depiction of the lesions. Conclusions This study illustrated the applicability of texture feature maps to the recognition of demineralized spots on the tooth surface debilitated by caries and identified the best performing techniques.

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“…To address this issue, we enabled the calculation of features for images with quantized pixel values, following the qMaZda approach. It is proved that a reduction in image depth increases the texture utility [27][28][29].…”

Section: Textural Featuresmentioning

confidence: 99%

Sex Differentiation of Trabecular Bone Structure Based on Textural Analysis of Pelvic Radiographs

Kamiński,

Nurzynska,

Kwiecień

et al. 2024

JCM

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Objectives: The purpose of this paper is to assess the determination of male and female sex from trabecular bone structures in the pelvic region. The study involved analyzing digital radiographs for 343 patients and identifying fourteen areas of interest based on their medical significance, with seven regions on each side of the body for symmetry. Methods: Textural parameters for each region were obtained using various methods, and a thorough investigation of data normalization was conducted. Feature selection approaches were then evaluated to determine a small set of the most representative features, which were input into several classification machine learning models. Results: The findings revealed a sex-dependent correlation in the bone structure observed in X-ray images, with the degree of dependency varying based on the anatomical location. Notably, the femoral neck and ischium regions exhibited distinctive characteristics between sexes. Conclusions: This insight is crucial for medical professionals seeking to estimate sex dependencies from such image data. For these four specific areas, the balanced accuracy exceeded 70%. The results demonstrated symmetry, confirming the genuine dependencies in the trabecular bone structures.

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On the Influence of Image Features Wordlength Reduction on Texture Classification

Cited by 9 publications

References 17 publications

Delineation of Cocoa Agroforests Using Multiseason Sentinel-1 SAR Images: A Low Grey Level Range Reduces Uncertainties in GLCM Texture-Based Mapping

Delineation of Cocoa Agroforests Using Multiseason Sentinel-1 SAR Images: A Low Grey Level Range Reduces Uncertainties in GLCM Texture-Based Mapping

Caries detection enhancement using texture feature maps of intraoral radiographs

Sex Differentiation of Trabecular Bone Structure Based on Textural Analysis of Pelvic Radiographs

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