Automation of Iron Difficiency Anemia Blue and Red Cell Number Calculating by Intictinal villi Tissue Slide Images Enhancing and Processing

Alhadidi, Basim; Fakhouri, Hussam N.

doi:10.1109/iccsit.2008.105

Cited by 4 publications

(1 citation statement)

References 2 publications

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“…This algorithm will count the number blue and the red stained cells blood cells that contain iron in each villi alone. And also calculate the percentage of blue cells and red cells in the image [3].…”

Section: Related Workmentioning

confidence: 99%

The Discrimination of Red Blood Cells Infected by Hereditary Hemolytic Anemia

Al-Talib¹,

Mohameed²

2013

AL-Rafidain Journal of Computer Sciences and Mathematics

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This paper presents a medical application based on digital image processing and Artificial Neural Network (ANN), which can recognize three types of Hereditary Hemolytic Anemia (HHA) that affect the Red Blood Cells (RBCs) and change their shape. Three Feed Forward Back Propagation Learning (FFBBL) Neural Networks are used in hierarchical approach to achieve this goal. The essence of this research is to segment each Red Blood Cell in a separate image and then extract some interesting features from each image in order to present them to the neural networks. The latter will, in turn, take the decision whether the RBC is infected or not. The results showed a recognition rate 92.38 %.

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Section: Related Workmentioning

confidence: 99%

The Discrimination of Red Blood Cells Infected by Hereditary Hemolytic Anemia

Al-Talib¹,

Mohameed²

2013

AL-Rafidain Journal of Computer Sciences and Mathematics

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A cognitive deep learning approach for medical image processing

Fakhouri,

Alawadi,

Awaysheh

et al. 2024

Sci Rep

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In ophthalmic diagnostics, achieving precise segmentation of retinal blood vessels is a critical yet challenging task, primarily due to the complex nature of retinal images. The intricacies of these images often hinder the accuracy and efficiency of segmentation processes. To overcome these challenges, we introduce the cognitive DL retinal blood vessel segmentation (CoDLRBVS), a novel hybrid model that synergistically combines the deep learning capabilities of the U-Net architecture with a suite of advanced image processing techniques. This model uniquely integrates a preprocessing phase using a matched filter (MF) for feature enhancement and a post-processing phase employing morphological techniques (MT) for refining the segmentation output. Also, the model incorporates multi-scale line detection and scale space methods to enhance its segmentation capabilities. Hence, CoDLRBVS leverages the strengths of these combined approaches within the cognitive computing framework, endowing the system with human-like adaptability and reasoning. This strategic integration enables the model to emphasize blood vessels, accurately segment effectively, and proficiently detect vessels of varying sizes. CoDLRBVS achieves a notable mean accuracy of 96.7%, precision of 96.9%, sensitivity of 99.3%, and specificity of 80.4% across all of the studied datasets, including DRIVE, STARE, HRF, retinal blood vessel and Chase-DB1. CoDLRBVS has been compared with different models, and the resulting metrics surpass the compared models and establish a new benchmark in retinal vessel segmentation. The success of CoDLRBVS underscores its significant potential in advancing medical image processing, particularly in the realm of retinal blood vessel segmentation.

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