Extraction of Nucleolus Candidate Zone in White Blood Cells of Peripheral Blood Smear Images Using Curvelet Transform

Soltanzadeh, Ramin; Rabbani, Hossein; Talebi, Ardeshir

doi:10.1155/2012/574184

Cited by 21 publications

(7 citation statements)

References 20 publications

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“…Where (̅, ̅) is the coordinate of the center point, (xi,yi) is the coordinate of the pixels with the same label and N is the number of the pixels of a connected component. In former studies, it was shown that in a spindle-shaped object (which is the ordinary shape of a fish), the boundary pixels' distances vary significantly relative to the center of the component [26] [27]. Therefore, if the variation of the boundary pixels' distance with respect to a component center was less than a certain threshold, that component was considered not to be a fish and was eliminated from the frame.…”

Section: Size and Shape Filtermentioning

confidence: 99%

A Prototype System for Real-Time Monitoring of Arctic Char in Indoor Aquaculture Operations: Possibilities & Challenges

Soltanzadeh

Hardy²,

McLeod

et al. 2020

IEEE Access

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In this exploratory study, we studied and qualitatively evaluated a prototype video data collection system to capture and analyze fish behavior in a small-scale indoor aquaculture operation. The research objective was to design and develop a hardware / software system that would have the potential to capture meaningful data from which to extract fish size, swim trajectory, and swim velocity, ultimately as information toward an assessment of fish health. The initial work presented in this paper discusses the development choices of the prototype system, including various combinations of lighting and camera positions both inside and outside of the aquaculture tanks, and several post-processing techniques to isolate fish in video, calibrate the distance from camera to fish through water, and infer fish trajectories and swim velocities. Preliminary results provided a qualitative assessment of such a system. Specific results on the system's ability to detect fishes' positions, trajectories, and velocities are presently limited to observational outcomes and descriptive statistics rather than large-scale quantitative analysis. The present work lays a foundation for a future commercially hardened system that would be required for the collection of larger datasets, which would in turn facilitate the future development of machine learning (ML) algorithms to begin to statistically correlate data to fish conditions and behaviors in near-real time. INDEX TERMS Aquaculture, arctic char, fish monitoring, video monitoring, image processing.

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Section: Size and Shape Filtermentioning

confidence: 99%

A Prototype System for Real-Time Monitoring of Arctic Char in Indoor Aquaculture Operations: Possibilities & Challenges

Soltanzadeh

Hardy²,

McLeod

et al. 2020

IEEE Access

Self Cite

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“…Several researchers have proposed many methods to achieve accurate segmentation results. Many researchers used clustering approaches to segment WBC from microscopic images such as K‐means clustering technique in other works, 7–10 and Fuzzy C‐means in the work of Viswanathan 11 . However, a fast and robust segmentation of WBCs still remains a challenging task 12 …”

Section: Introductionmentioning

confidence: 99%

Accelerating white blood cells image segmentation using GPUs

Baker

Alsmirat

Balhaf

et al. 2019

Concurrency and Computation

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White Blood Cell (WBC) segmentation is one of the important topics in the medical image processing field. Many researchers proposed several clustering approaches to segment WBC from blood smear microscopic images. However, a fast and robust segmentation of WBCs is still a challenging task. In this work, we propose parallel algorithms that utilize the parallelism capabilities of the Graphics Processing Units (GPUs) to accelerate the segmentation of WBC from microscopic images. In this research, we implement the main image segmentation clustering algorithms using one thread that we run on a single CPU (sequential implementation) and using multiple threads that we run on both the CPU and the GPU (hybrid CPU-GPU). We focus our work on the most common four segmentation algorithms: Standard K-means (SKM), Adaptive K-means (AKM), Fuzzy C-means (FCM), and Fuzzy Possibilistic C-means (FPCM). We implement these algorithms and the pre-processing steps for WBC image segmentation in CUDA programming to take the advantages of the large number of cores in GPUs. In this work, our hybrid implementation accelerated the four studied sequential algorithms by 4X, 3.8X, 3.4X, and 3.4X, respectively, without affecting WBC segmentation quality.

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“…Soltanzadeh et al . () extracted nuclei by means of K‐means method and then applied curvelet transform to extract the candidate locations of chromatins and nucleoli. Also, Sarrafzadeh et al .…”

Section: Introductionmentioning

confidence: 99%

Automatic recognition of myeloma cells in microscopic images using bottleneck algorithm, modified watershed and SVM classifier

Saeedizadeh

Dehnavi

Talebi

et al. 2016

Journal of Microscopy

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Plasma cells are developed from B lymphocytes, a type of white blood cells that is generated in the bone marrow. The plasma cells produce antibodies to fight with bacteria and viruses and stop infection and disease. Multiple myeloma is a cancer of plasma cells that collections of abnormal plasma cells (myeloma cells) accumulate in the bone marrow. The definitive diagnosis of multiple myeloma is done by searching for myeloma cells in the bone marrow slides through a microscope. Diagnosis of myeloma cells from bone marrow smears is a subjective and time-consuming task for pathologists. Also, because of depending on final decision on human eye and opinion, error risk in decision may occur. Sometimes, existence of infection in body causes plasma cell's increment which could be diagnosed wrongly as multiple myeloma. The computer diagnostic process will reduce the diagnostic time and also can be worked as a second opinion for pathologists. This study presents a computer-aided diagnostic method for myeloma cells diagnosis from bone marrow smears. At first, white blood cells consist of plasma cells and other marrow cells are separated from the red blood cells and background. Then, plasma cells are detected from other marrow cells by feature extraction and series of decision rules. Finally, normal plasma cells and myeloma cells could be classified easily by a classifier. This algorithm is applied on 50 digital images that are provided from bone marrow aspiration smears. These images contain 678 cells: 132 normal plasma cells, 256 myeloma cells and 290 other types of marrow cells. Applying the computer-aided diagnostic method for identifying myeloma cells on provided database showed a sensitivity of 96.52%; specificity of 93.04% and precision of 95.28%.

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Extraction of Nucleolus Candidate Zone in White Blood Cells of Peripheral Blood Smear Images Using Curvelet Transform

Cited by 21 publications

References 20 publications

A Prototype System for Real-Time Monitoring of Arctic Char in Indoor Aquaculture Operations: Possibilities & Challenges

A Prototype System for Real-Time Monitoring of Arctic Char in Indoor Aquaculture Operations: Possibilities & Challenges

Accelerating white blood cells image segmentation using GPUs

Automatic recognition of myeloma cells in microscopic images using bottleneck algorithm, modified watershed and SVM classifier

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