Classification of texture using random box counting and binarization methods

The heterogeneous texture classifications with the complexity of structures provide variety of possibilities in image processing, as an example of the multifractal analysis features. The task of texture analysis is a highly significant field of study in the field of machine vision. Most of the real-life surfaces exhibit textures and an efficiently modelled vision system must have the ability to deal with this variety of surfaces. A considerable number of surfaces maintain a self-similarity quality as well as statistical roughness at different scales. Fractals could provide a great deal of advantages; also, they are popular in the process of modelling these properties in the tasks related to the field of image processing. With two distinct methods, this paper presents classification of texture using random box counting and binarization methods calculate the estimation measures of the fractal dimension BCM. There methods are the banalization and random selecting boxes. The classification of the white blood cells is presented in this paper based on the texture if it is normal or abnormal with the use of a number of various methods.

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Comparison of traditional image processing and deep learning approaches for classification of white blood cells in peripheral blood smear images

Journal of Applied Biomedicine ◽

10.1016/j.bbe.2019.01.005 ◽

2019 ◽

Vol 39 (2) ◽

pp. 382-392 ◽

Cited By ~ 16

Author(s):

Roopa B. Hegde ◽

Keerthana Prasad ◽

Harishchandra Hebbar ◽

Brij Mohan Kumar Singh

Keyword(s):

Image Processing ◽

Deep Learning ◽

Peripheral Blood ◽

Blood Cells ◽

Blood Smear ◽

White Blood Cells ◽

Peripheral Blood Smear ◽

Learning Approaches ◽

Traditional Image

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Klasifikasi Sel Darah Putih Berdasarkan Ciri Warna dan Bentuk dengan Metode K-Nearest Neighbor (K-NN)

IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) ◽

10.22146/ijeis.15254 ◽

2016 ◽

Vol 6 (2) ◽

pp. 151 ◽

Cited By ~ 1

Author(s):

Mizan Nur Khasanah ◽

Agus Harjoko ◽

Ika Candradewi

Keyword(s):

Image Processing ◽

Blood Cells ◽

Nearest Neighbor ◽

White Blood Cells ◽

Processing Technique ◽

Image Processing Technique ◽

K Nearest Neighbor ◽

Traditional Procedure ◽

Early Diagnose

The traditional procedure of classification of blood cells using a microscope in the laboratory of hematology to obtain information types of blood cells. It has become a cornerstone in the laboratory of hematology to diagnose and monitor hematologic disorders. However, the manual procedure through a series of labory test can take a while. Thresfore, this research can be helpful in the early stages of the classification of white blood cells automatically in the medical field.Efforts to overcome the length of time and for the purposes of early diagnose can use the image processing technique based on morphology of blood cells. This research aims to classify the white blood cells based on cell morphology with the k-nearest neighbor (knn). Image processing algorithms used hough circle, thresholding, feature extraction, then to the process of classification was used the method of k-nearest neighbor (knn).In the process of testing used 100 images to be aware of its kind. The test results showed segmentation accuracy of 78% and testing the classification of 64%.

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Supramolecular Fractal Growth of Self-Assembled Fibrillar Networks

Gels ◽

10.3390/gels7020046 ◽

2021 ◽

Vol 7 (2) ◽

pp. 46

Author(s):

Pedram Nasr ◽

Hannah Leung ◽

France-Isabelle Auzanneau ◽

Michael A. Rogers

Keyword(s):

Fractal Dimension ◽

Crystallization Temperature ◽

Cayley Tree ◽

Fractal Dimensions ◽

Self Similarity ◽

Avrami Model ◽

Cluster Aggregation ◽

Box Counting ◽

Self Assembled ◽

Limited Diffusion

Complex morphologies, as is the case in self-assembled fibrillar networks (SAFiNs) of 1,3:2,4-Dibenzylidene sorbitol (DBS), are often characterized by their Fractal dimension and not Euclidean. Self-similarity presents for DBS-polyethylene glycol (PEG) SAFiNs in the Cayley Tree branching pattern, similar box-counting fractal dimensions across length scales, and fractals derived from the Avrami model. Irrespective of the crystallization temperature, fractal values corresponded to limited diffusion aggregation and not ballistic particle–cluster aggregation. Additionally, the fractal dimension of the SAFiN was affected more by changes in solvent viscosity (e.g., PEG200 compared to PEG600) than crystallization temperature. Most surprising was the evidence of Cayley branching not only for the radial fibers within the spherulitic but also on the fiber surfaces.

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Classification of White Blood Cells Based on Cell Diameter, Specific Membrane Capacitance and Cytoplasmic Conductivity Leveraging Microfluidic Constriction Channel

2021 21st International Conference on Solid-State Sensors, Actuators and Microsystems (Transducers) ◽

10.1109/transducers50396.2021.9495490 ◽

2021 ◽

Author(s):

Huiwen Tan ◽

Minruihong Wang ◽

Yi Zhang ◽

Xukun Huang ◽

Deyong Chen ◽

...

Keyword(s):

Blood Cells ◽

White Blood Cells ◽

Membrane Capacitance ◽

Cell Diameter ◽

Specific Membrane

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New Segmentation and Feature Extraction Algorithm for the Classification of White Blood Cells in Peripheral Smear Images

10.1101/2021.04.29.441751 ◽

2021 ◽

Author(s):

Eslam Tavakoli ◽

Ali Ghaffari ◽

Seyedeh-Zahra Mousavi Kouzehkanan ◽

Reshad Hosseini

Keyword(s):

Blood Cells ◽

White Blood Cells ◽

Dice Similarity Coefficient ◽

Second Phase ◽

Peripheral Smear ◽

Color Features ◽

Svm Model ◽

Extraction Algorithm ◽

Processing Techniques

This article addresses a new method for classification of white blood cells (WBCs) using image processing techniques and machine learning methods. The proposed method consists of three steps: detecting the nucleus and cytoplasm, extracting features, and classification. At first, a new algorithm is designed to segment the nucleus. For the cytoplasm to be detected, only a part of it which is located inside the convex hull of the nucleus is involved in the process. This attitude helps us overcome the difficulties of segmenting the cytoplasm. In the second phase, three shape and four novel color features are devised and extracted. Finally, by using an SVM model, the WBCs are classified. The segmentation algorithm can detect the nucleus with a dice similarity coefficient of 0.9675. The proposed method can categorize WBCs in Raabin-WBC, LISC, and BCCD datasets with accuracies of 94.47 %, 92.21 %, and 94.20 %, respectively. It is worth mentioning that the hyperparameters of the classifier are fixed only with Raabin-WBC dataset, and these parameters are not readjusted for LISC and BCCD datasets. The obtained results demonstrate that the proposed method is robust, fast, and accurate.

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Klasifikasi Sel Darah Putih dan Sel Limfoblas Menggunakan Metode Multilayer Perceptron Backpropagation

IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) ◽

10.22146/ijeis.49943 ◽

2019 ◽

Vol 9 (2) ◽

pp. 173

Author(s):

Apri Nur Liyantoko ◽

Ika Candradewi ◽

Agus Harjoko

Keyword(s):

Blood Cell ◽

White Blood Cell ◽

Blood Cells ◽

White Blood Cells ◽

Back Propagation ◽

Cell Types ◽

Diagnostic Process ◽

The Subject ◽

Processing Techniques

Leukemia is a type of cancer that is on white blood cell. This disease are characterized by abundance of abnormal white blood cell called lymphoblast in the bone marrow. Classification of blood cell types, calculation of the ratio of cell types and comparison with normal blood cells can be the subject of diagnosing this disease. The diagnostic process is carried out manually by hematologists through microscopic image. This method is likely to provide a subjective result and time-consuming.The application of digital image processing techniques and machine learning in the process of classifying white blood cells can provide more objective results. This research used thresholding method as segmentation and multilayer method of back propagation perceptron with variations in the extraction of textural features, geometry, and colors. The results of segmentation testing in this study amounted to 68.70%. Whereas the classification test shows that the combination of feature extraction of GLCM features, geometry features, and color features gives the best results. This test produces an accuration value 91.43%, precision value of 50.63%, sensitivity 56.67%, F1Score 51.95%, and specitifity 94.16%.

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Segmentation of Leukemia Cells Using Clustering

International Journal of Synthetic Emotions ◽

10.4018/ijse.2019070103 ◽

2019 ◽

Vol 10 (2) ◽

pp. 39-48

Author(s):

Eman Mostafa ◽

Heba A. Tag El-Dien

Keyword(s):

Bone Marrow ◽

Cancer Cells ◽

Blood Cells ◽

White Blood Cells ◽

Leukemia Cells ◽

Microscopic Image ◽

Processing Stage ◽

Clustering Techniques ◽

Fuzzy C Means

Leukemia is a blood cancer which is defined as an irregular augment of undeveloped white blood cells called “blasts.” It develops in the bone marrow, which is responsible for blood cell generation including leukocytes and white blood cells. The early diagnosis of leukemia greatly helps in the treatment. Accordingly, researchers are interested in developing advanced and accurate automated techniques for localizing such abnormal blood cells. Subsequently, image segmentation becomes an important image processing stage for successful feature extraction and classification of leukemia in further stages. It aims to separate cancer cells by segmenting the microscopic image into background and cancer cells that are known as the region of interested (ROI). In this article, the cancer blood cells were segmented using two separated clustering techniques, namely the K-means and Fuzzy-c-means techniques. Then, the results of these techniques were compared to in terms of different segmentation metrics, such as the Dice, Jac, specificity, sensitivity, and accuracy. The results proved that the k-means provided better performance in leukemia blood cells segmentation as it achieved an accuracy of 99.8% compared to 99.6% with the fuzzy c-means.

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Image Processing Applications Based on Texture and Fractal Analysis

Applied Signal and Image Processing ◽

10.4018/978-1-60960-477-6.ch014 ◽

2011 ◽

pp. 226-250 ◽

Cited By ~ 6

Author(s):

Radu Dobrescu ◽

Dan Popescu

Keyword(s):

Fractal Dimension ◽

Texture Classification ◽

Malignant Tumour ◽

Classification Performance ◽

Textured Surfaces ◽

Box Counting ◽

Image Partition ◽

Fractal Analyses ◽

Occurrence Matrix

Texture analysis research attempts to solve two important kinds of problems: texture segmentation and texture classification. In some applications, textured image segmentation can be solved by classification of small regions obtained from image partition. Two classes of features are proposed in the decision theoretic recognition problem for textured image classification. The first class derives from the mean co-occurrence matrices: contrast, energy, entropy, homogeneity, and variance. The second class is based on fractal dimension and is derived from a box-counting algorithm. For the purpose of increasing texture classification performance, the notions “mean co-occurrence matrix” and “effective fractal dimension” are introduced and utilized. Some applications of the texture and fractal analyses are presented: road analysis for moving objective, defect detection in textured surfaces, malignant tumour detection, remote land classification, and content based image retrieval. The results confirm the efficiency of the proposed methods and algorithms.

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