Rotation Invariant Co-occurrence Matrix Features

Putzu, Lorenzo; Ruberto, Cecilia Di

doi:10.1007/978-3-319-68560-1_35

Cited by 14 publications

(13 citation statements)

References 17 publications

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“…In our experiment, we compared two approaches for rotation-invariant artifacts detection; training data augmentation by including rotated images and the use of rotation-invariant features 46), 47) . We tested the method in 47) , in which principal component analysis (PCA) was applied to the circulant matrix of In the case of tissue fold, we consider the fact that the tissue fold poses a higher thickness compared to the regular tissue area and have a higher saturationluminance difference 38) . Then, we use the number of high saturation and low luminance pixels and the (1) ITE Trans.…”

Section: Overview Of the Proposed Methodsmentioning

confidence: 99%

[Paper] Automatic Quality Evaluation of Whole Slide Images for the Practical Use of Whole Slide Imaging Scanner

Shakhawat

Nakamura

Kimura

et al. 2020

MTA

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Previously, a referenceless quality evaluation method (RQM) was proposed by Hashimoto et al. to evaluate the quality of the scanned image 7) for the purpose of Abstract A whole slide imaging (WSI) scanner scans pathological-specimens to produce digital images for monitor-based diagnosis and analysis. However, the image quality is sometimes insufficient due to focus-error or noise, in which case the slide needs to be rescanned. In previous work, a referenceless quality evaluation technique was proposed, but some artifacts (i.e. tissue-fold, air-bubble) were detected as false positives. Those artifacts need to be ignored in determining whether rescanning is necessary or not, because they are not caused in the scanning but slide preparation stage. This paper proposes a method for a more practical system to assess WSI quality by distinguishing the origins of quality degradation; the focus-error or noise caused by the scanner and the artifact occurred in the slide preparation. In the method, a support vector machine detects artifacts first, and then quality is evaluated excluding artifact regions. The effectiveness of the proposed system has been experimentally demonstrated.

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Section: Overview Of the Proposed Methodsmentioning

confidence: 99%

[Paper] Automatic Quality Evaluation of Whole Slide Images for the Practical Use of Whole Slide Imaging Scanner

Shakhawat

Nakamura

Kimura

et al. 2020

MTA

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“…We also remark that in in our experiments we use GLCM computed with distance 1, because our aim is to show how the invariant moments can be more discriminative if extracted from a different image representation, rather than obtaining perfect texture classification results. Indeed, in a recent work [32] we demonstrated that a larger distance may lead to better classification performances. Also, GLCM computed with distance 1 are not well suited to characterize coarse textures or even objects, both present on Vectorial and ALOT datasets (see Figure 4), but performs better in describing fine textures and close patterns.…”

Section: Texture Analysismentioning

confidence: 83%

“…To further highlight the performance of the approaches studied in this paper, we compared them to some widely used state-of-the-art descriptors for texture classification. We computed the rotation invariant GLCM features as proposed in [32], the rotation invariant LBP (LBP-RI) [29] with a 3 × 3 pixels neighborhood and distance 1, and the Convolutional Neural Network (CNN) features from three different well known network architectures. The first architecture is AlexNet [19] that gained popularity for its good performance in many classification tasks.…”

Section: Texture Analysismentioning

confidence: 99%

Fast and accurate computation of orthogonal moments for texture analysis

Ruberto

Putzu

Rodriguez

2018

Pattern Recognition

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In this work we describe a fast and stable algorithm for the computation of the orthogonal moments of an image. Indeed, orthogonal moments are characterized by a high discriminative power, but some of their possible formulations are characterized by a large computational complexity, which limits their real-time application. This paper describes in detail an approach based on recurrence relations, and proposes an optimized Matlab implementation of the corresponding computational procedure, aiming to solve the above limitations and put at the community's disposal an efficient and easy to use software. In our experiments we evaluate the effectiveness of the recurrence formulation, as well as its performance for the reconstruction task, in comparison to the closed form representation, often used in the literature. The results show a sensible reduction in the computational complexity, together with a greater accuracy in reconstruction. In order to assess and compare the accuracy of the computed moments in texture analysis, we perform classification experiments on six wellknown databases of texture images. Again, the recurrence formulation performs better in classification than the closed form representation. More importantly, if computed from the GLCM of the image using the proposed stable procedure, the orthogonal moments outperform in some situations some of the most diffused state-of-the-art descriptors for texture classification.

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“…The Texture Features computed were the rotation invariant Gray Level Co-occurrence Matrix (GLCM) features, as proposed in [46], and the rotation invariant Local Binary Pattern (LBP) features [47]. In both cases we focused on fine textures, thus we computed four GLCMs with d = 1 and θ = [0 • , 45 • , 90 • , 135 • ], and the LBP map in the neighbourhood identified by r and n equal to 1 and 8 respectively.…”

Section: Hand-crafted Image Descriptorsmentioning

confidence: 99%

“…In both cases we focused on fine textures, thus we computed four GLCMs with d = 1 and θ = [0 • , 45 • , 90 • , 135 • ], and the LBP map in the neighbourhood identified by r and n equal to 1 and 8 respectively. From the GLCMs we extracted thirteen features [48] and converted into rotationally invariant ones Har ri (for more details see [46]). The LBP map is then converted into a rotationally invariant one, and its histogram is used as a feature vector LBP ri [47].…”

Section: Hand-crafted Image Descriptorsmentioning

confidence: 99%

On the Effectiveness of Leukocytes Classification Methods in a Real Application Scenario

Loddo

Putzu

2021

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Automating the analysis of digital microscopic images to identify the cell sub-types or the presence of illness has assumed a great importance since it aids the laborious manual process of review and diagnosis. In this paper, we have focused on the analysis of white blood cells. They are the body’s main defence against infections and diseases and, therefore, their reliable classification is very important. Current systems for leukocyte analysis are mainly dedicated to: counting, sub-types classification, disease detection or classification. Although these tasks seem very different, they share many steps in the analysis process, especially those dedicated to the detection of cells in blood smears. A very accurate detection step gives accurate results in the classification of white blood cells. Conversely, when detection is not accurate, it can adversely affect classification performance. However, it is very common in real-world applications that work on inaccurate or non-accurate regions. Many problems can affect detection results. They can be related to the quality of the blood smear images, e.g., colour and lighting conditions, absence of standards, or even density and presence of overlapping cells. To this end, we performed an in-depth investigation of the above scenario, simulating the regions produced by detection-based systems. We exploit various image descriptors combined with different classifiers, including CNNs, in order to evaluate which is the most suitable in such a scenario, when performing two different tasks: Classification of WBC subtypes and Leukaemia detection. Experimental results have shown that Convolutional Neural Networks are very robust in such a scenario, outperforming common machine learning techniques combined with hand-crafted descriptors. However, when exploiting appropriate images for model training, even simpler approaches can lead to accurate results in both tasks.

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Rotation Invariant Co-occurrence Matrix Features

Cited by 14 publications

References 17 publications

[Paper] Automatic Quality Evaluation of Whole Slide Images for the Practical Use of Whole Slide Imaging Scanner

[Paper] Automatic Quality Evaluation of Whole Slide Images for the Practical Use of Whole Slide Imaging Scanner

Fast and accurate computation of orthogonal moments for texture analysis

On the Effectiveness of Leukocytes Classification Methods in a Real Application Scenario

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