Multiresolution local binary pattern variants based texture feature extraction techniques for efficient classification of microscopic images of hardwood species

Yadav, Arvind R.; Anand, R. S.; Dewal, M. L.; Gupta, Sangeeta

doi:10.1016/j.asoc.2015.03.039

Cited by 44 publications

(22 citation statements)

References 39 publications

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“…This may be explained by our analysis only extracting local information. Multi-resolution analysis is often performed with wavelet transforms [31,32], and it may be helpful for extracting features at various scales, as reported previously for wood [18,19]. If we focus more strongly on the linkages between image features and anatomy, then microscopic images may be more appropriate than stereograms.…”

Section: Resultsmentioning

confidence: 99%

Texture analysis of stereograms of diffuse-porous hardwood: identification of wood species used in Tripitaka Koreana

et al. 2017

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Section: Resultsmentioning

confidence: 99%

Texture analysis of stereograms of diffuse-porous hardwood: identification of wood species used in Tripitaka Koreana

et al. 2017

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“…Traditional manually designed feature descriptors in (Yadav et al, 2015), such as gradient operators and filter banks, are not able to capture if complex variation related in frequency is found in medical images (Rose et al, 2010). This paves the way for designing efficient image descriptors.…”

Section: Introductionmentioning

confidence: 99%

Classification of Breast Tumor in Mammogram Images Using Unsupervised Feature Learning

Ibrahim

Baharudin²,

Said³

et al. 2016

American Journal of Applied Sciences

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Abstract:In this study, we propose a learning-based approach using feature learning to minimize the manual effort required to extract features. Firstly, we extracted features from equally spaced sub-patches covering the input Region of Interest (ROI). The dimensionality of the extracted features is reduced using max-pooling. Furthermore, spherical k-means clustering coupled with max pooling (k-means-max pooling) is compared with wellknown feature extraction method namely Bag-of-features. The resulting feature vector is fed to two different classifiers: K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM). The performance of these classifiers is evaluated to use of Receiver Operating Characteristics (ROC). Our results show that k-means-max pooling, combined with K-NN, achieved good performance with an average classification accuracy of 98.19%, sensitivity of 97.09% and specificity of 99.35%.

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“…The systems available only analyse the transverse section (ignoring the other two sections) as it is the section that contains most information. Two main tasks are included in existing systems: image processing techniques to segment objects of interest [1,2,6,8,11] and automatic feature extraction for species classification [3,9,10,13,[16][17][18]. However, none of the available systems joins the segmentation task and the species classification task with features that are meaningful in a biological sense to perform a species identification.…”

Section: Introductionmentioning

confidence: 99%

Modified DBSCAN Algorithm for Microscopic Image Analysis of Wood

Martins

Marçal

Pissarra

2019

Pattern Recognition and Image Analysis

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The analysis of the intern anatomy of wood samples for species identification is a complex task that only experts can perform accurately. Since there are not many experts in the world and their training can last decades, there is great interest in developing automatic processes to extract high-level information from microscopic wood images. The purpose of this work was to develop algorithms that could provide meaningful information for the classification process. The work focuses on hardwoods, which have a very diverse anatomy including many different features. The ray width is one of such features, with high diagnostic value, which is visible on the tangential section. A modified distance function for the DBSCAN algorithm was developed to identify clusters that represent rays, in order to count the number of cells in width. To test both the segmentation and the modified DBSCAN algorithms, 20 images were manually segmented, obtaining an average Jaccard index of 0.66 for the segmentation and an average index M = 0.78 for the clustering task. The final ray count had an accuracy of 0.91.

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Multiresolution local binary pattern variants based texture feature extraction techniques for efficient classification of microscopic images of hardwood species

Cited by 44 publications

References 39 publications

Texture analysis of stereograms of diffuse-porous hardwood: identification of wood species used in Tripitaka Koreana

Texture analysis of stereograms of diffuse-porous hardwood: identification of wood species used in Tripitaka Koreana

Classification of Breast Tumor in Mammogram Images Using Unsupervised Feature Learning

Modified DBSCAN Algorithm for Microscopic Image Analysis of Wood

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