Thresholding-based White Blood Cells Segmentation from Microscopic Blood Images

Mohammed, Zhana Fidakar; Abdulla, Alan Anwer

doi:10.21928/uhdjst.v4n1y2020.pp9-17

Cited by 29 publications

(14 citation statements)

References 13 publications

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“…Zhana et al proposed a new algorithm for segmenting WBCs from microscopic blood images based on the thresholding segmentation technique and the authors compared their results with the most commonly used segmentation technique which is known as colour‐k‐means clustering. As the authors reported, their thresholding‐based proposed segmentation technique outperforms the colour‐k‐means clustering [21]. Continuously, in 2020, another CAD system for recognising the blast cells from bone marrow images in which the leukaemia can be identified was developed by Nikitaev et al To separate the nucleus of the cells from the input images, two segmentation techniques were used.…”

Section: Literature Reviewmentioning

confidence: 99%

Efficient computer‐aided diagnosis technique for leukaemia cancer detection

Abdulla

2020

IET image process

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Computer‐aided diagnosis (CAD) is a common tool for the detection of diseases, particularly different types of cancers, based on medical images. Digital image processing thus plays a significant role in the processing and analysis of medical images for diseases identification and detection purposes. In this study, an efficient CAD system for the acute lymphoblastic leukaemia (ALL) detection is proposed. The proposed approach entails two phases. In the first phase, the white blood cells (WBCs) are segmented from the microscopic blood image. The second phase involves extracting important features, such as shape and texture features from the segmented cells. Eventually, on the extracted features, Naïve Bayes and k‐nearest neighbour classifier techniques are implemented to identify the segmented cells into normal and abnormal cells. The performance of the proposed approach has been assessed through comprehensive experiments carried out on the well‐known ALL‐IDB data set of microscopic blood images. The experimental results demonstrate the superior performance of the proposed approach over the state‐of‐the‐art in terms of accuracy rate in which achieved 98.7%.

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Section: Literature Reviewmentioning

confidence: 99%

Efficient computer‐aided diagnosis technique for leukaemia cancer detection

Abdulla

2020

IET image process

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“…Digital image processing plays a significant role in various areas such as medical image processing [1], image inpainting [2], pattern recognition, biometrics, content-based image retrieval (CBIR), image compression, information hiding [3], and multimedia security [4]. The retrieval of similar images from a large range of images is becoming a serious challenge with the advent of digital communication technology and the growing use of the Internet.…”

Section: Introductionmentioning

confidence: 99%

An Improved Content Based Image Retrieval Technique by Exploiting Bi-layer Concept

Salih

Abdulla²

2021

UHD J SCI TECH

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Applications for retrieving similar images from a large collection of images have increased significantly in various fields with the rapid advancement of digital communication technologies and exponential evolution in the usage of the Internet. Content-based image retrieval (CBIR) is a technique to find similar images on the basis of extracting the visual features such as color, texture, and/or shape from the images themselves. During the retrieval process, features and descriptors of the query image are compared to those of the images in the database to rank each indexed image accordingly to its distance to the query image. This paper has developed a new CBIR technique which entails two layers, called bi-layers. In the first layer, all images in the database are compared to the query image based on the bag of features (BoF) technique, and hence, the M most similar images to the query image are retrieved. In the second layer, the M images obtained from the first layer are compared to the query image based on the color, texture, and shape features to retrieve the N number of the most similar images to the query image. The proposed technique has been evaluated using a well-known dataset of images called Corel-1K. The obtained results revealed the impact of exploring the idea of bi-layers in improving the precision rate in comparison to the current state-of-the-art techniques in which achieved precision rate of 82.27% and 76.13% for top-10 and top-20, respectively.

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“…Beside content-based image retrieval (CBIR), digital image processing plays a vital role in numerous areas such as processing and analyzing medical image [1], image inpainting [2], pattern recognition [3], biometrics [4], multimedia security [5], and information hiding [6]. In the area of image processing and computer vision, CBIR has grown increasingly as an advanced research topic.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Two-layer based Technique for Content-based Image Retrieval

Salih

Abdulla

2021

UHD J SCI TECH

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The rapid advancement and exponential evolution in the multimedia applications raised the attentional research on content-based image retrieval (CBIR). The technique has a significant role for searching and finding similar images to the query image through extracting the visual features. In this paper, an approach of two layers of search has been developed which is known as two-layer based CBIR. The first layer is concerned with comparing the query image to all images in the dataset depending on extracting the local feature using bag of features (BoF) mechanism which leads to retrieve certain most similar images to the query image. In other words, first step aims to eliminate the most dissimilar images to the query image to reduce the range of search in the dataset of images. In the second layer, the query image is compared to the images obtained in the first layer based on extracting the (texture and color)-based features. The Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP) were used as texture features. However, for the color features, three different color spaces were used, namely RGB, HSV, and YCbCr. The color spaces are utilized by calculating the mean and entropy for each channel separately. Corel-1K was used for evaluating the proposed approach. The experimental results prove the superior performance of the proposed concept of two-layer over the current state-of-the-art techniques in terms of precision rate in which achieved 82.15% and 77.27% for the top-10 and top-20, respectively.

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Thresholding-based White Blood Cells Segmentation from Microscopic Blood Images

Cited by 29 publications

References 13 publications

Efficient computer‐aided diagnosis technique for leukaemia cancer detection

Efficient computer‐aided diagnosis technique for leukaemia cancer detection

An Improved Content Based Image Retrieval Technique by Exploiting Bi-layer Concept

An Efficient Two-layer based Technique for Content-based Image Retrieval

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