Blood Cell Counting and Segmentation Using Image Processing Techniques

Chaudhary, Ayesha Hoor; Ikhlaq, Javeria; Iftikhar, Muhammad Aksam; Alvi, Maham

doi:10.1007/978-3-319-96139-2_9

Cited by 5 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, semantic segmentation was conducted by Chaudhary et al (2019) , Tran et al (2019) using SegNet architecture and VGG, respectively, for the segmentation and quantification of white blood cells (WBCs) and red blood cells (RBCs) in the ALL-IDB-I dataset. This architecture achieved an average accuracy of 93%.…”

Section: Literature Reviewmentioning

confidence: 99%

Blood cell image segmentation and classification: a systematic review

Shahzad,

Ali,

Shirazi

et al. 2024

PeerJ Computer Science

View full text Add to dashboard Cite

Background Blood diseases such as leukemia, anemia, lymphoma, and thalassemia are hematological disorders that relate to abnormalities in the morphology and concentration of blood elements, specifically white blood cells (WBC) and red blood cells (RBC). Accurate and efficient diagnosis of these conditions significantly depends on the expertise of hematologists and pathologists. To assist the pathologist in the diagnostic process, there has been growing interest in utilizing computer-aided diagnostic (CAD) techniques, particularly those using medical image processing and machine learning algorithms. Previous surveys in this domain have been narrowly focused, often only addressing specific areas like segmentation or classification but lacking a holistic view like segmentation, classification, feature extraction, dataset utilization, evaluation matrices, etc. Methodology This survey aims to provide a comprehensive and systematic review of existing literature and research work in the field of blood image analysis using deep learning techniques. It particularly focuses on medical image processing techniques and deep learning algorithms that excel in the morphological characterization of WBCs and RBCs. The review is structured to cover four main areas: segmentation techniques, classification methodologies, descriptive feature selection, evaluation parameters, and dataset selection for the analysis of WBCs and RBCs. Results Our analysis reveals several interesting trends and preferences among researchers. Regarding dataset selection, approximately 50% of research related to WBC segmentation and 60% for RBC segmentation opted for manually obtaining images rather than using a predefined dataset. When it comes to classification, 45% of the previous work on WBCs chose the ALL-IDB dataset, while a significant 73% of researchers focused on RBC classification decided to manually obtain images from medical institutions instead of utilizing predefined datasets. In terms of feature selection for classification, morphological features were the most popular, being chosen in 55% and 80% of studies related to WBC and RBC classification, respectively. Conclusion The diagnostic accuracy for blood-related diseases like leukemia, anemia, lymphoma, and thalassemia can be significantly enhanced through the effective use of CAD techniques, which have evolved considerably in recent years. This survey provides a broad and in-depth review of the techniques being employed, from image segmentation to classification, feature selection, utilization of evaluation matrices, and dataset selection. The inconsistency in dataset selection suggests a need for standardized, high-quality datasets to strengthen the diagnostic capabilities of these techniques further. Additionally, the popularity of morphological features indicates that future research could further explore and innovate in this direction.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Blood cell image segmentation and classification: a systematic review

Shahzad,

Ali,

Shirazi

et al. 2024

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…Methods for the detection and segmentation of lung nodules can be categorized into traditional and learning-based. Traditional methods rely on handcrafted feature extraction [2], often coupled with shallow classifiers or regressors. The main problem with such techniques is the manually designing feature extractors time consuming activity and features may be tailored to some specific dataset.…”

Section: Background and Related Workmentioning

confidence: 99%

UniToChest: A Lung Image Dataset for Segmentation of Cancerous Nodules on CT Scans

Chaudhry

Renzulli

Perlo

et al. 2022

Image Analysis and Processing – ICIAP 2022

View full text Add to dashboard Cite

Lung cancer has emerged as a major causes of death and early detection of lung nodules is the key towards early cancer diagnosis and treatment effectiveness assessment. Deep neural networks achieve outstanding results in tasks such as lung nodules detection, segmentation and classification, however their performance depends on the quality of the training images and on the training procedure. This paper introduces UniToChest , a dataset consisting Computed Tomography (CT) scans of 623 patients and 10071 lesions labelled for segmentation with nodules spanning different diameters ranges. Then, we propose a lung nodules segmentation scheme relying on a convolutional neural architecture that we also re-purpose for a nodule detection task. The experimental results show accurate segmentation of lung nodules across a wide diameter range and better detection accuracy over a traditional detection approach. The datasets and the code used in this paper are publicly made available as a baseline reference.

show abstract