Fast fully automatic heart fat segmentation in computed tomography datasets.

An Automated Method for Counting Red Blood Cells using Image Processing

Sickle cell anemia (SCA) is a serious hematological disorder, where affected patients are frequently hospitalized throughout a lifetime and even can cause death. The manual method of detecting and classifying abnormal cells of SCA patient blood film through a microscope is time-consuming, tedious, prone to error, and require a trained hematologist. The affected patient has many cell shapes that show important biomechanical characteristics. Hence, having an effective way of classifying the abnormalities present in the SCA disease will give a better insight into managing the concerned patient's life. This work proposed algorithm in two-phase firstly, automation of red blood cells (RBCs) extraction to identify the RBC region of interest (ROI) from the patient’s blood smear image. Secondly, deep learning AlexNet model is employed to classify and predict the abnormalities presence in SCA patients. The study was performed with (over 9,000 single RBC images) taken from 130 SCA patient each class having 750 cells. To develop a shape factor quantification and general multiscale shape analysis. We reveal that the proposed framework can classify 15 types of RBC shapes including normal in an automated manner with a deep AlexNet transfer learning model. The cell's name classification prediction accuracy, sensitivity, specificity, and precision of 95.92%, 77%, 98.82%, and 90% were achieved, respectively.

http://ijai.iaescore.com/index.php/IJAI/article/download/20420/13041

A deep learning AlexNet model for classification of red blood cells in sickle cell anemia

In the biomedicine field, blood cell analysis is the first step for diagnosis of many of the disease. The first test that is requested by a doctor is the CBC (Complete Blood cell Count). Microscopic image of blood stream contains three types of blood cells: Red Blood Cells (RBCs), White Blood Cells (WBCs) and platelets. Earlier counting of blood cell was done manually which was inaccurate and depends on operator’s skill. Counting of blood cells using image processing provides cost effective and accurate result than manual counting. During the counting process, the splitting of clumped cell is the most challenging issue. This paper represents segmentation and counting of RBCs and WBCs from microscopic blood sample images. Segmentation is done using Otsu’s thresholding and morphological operations. Counting of cells is done using geometric features of cells. RBCs contain clumped cells which make the task of counting of cells accurately very challenging. For counting of RBCs, two different methods are used: 1) Watershed segmentation 2) Circular Hough Transform. Comparison of both this method is shown for randomly selected images. The performance of counting methods is also analyzed by comparing it with results obtained by manual counts.

https://www.mecs-press.org/ijigsp/ijigsp-v8-n11/IJIGSP-V8-N11-5.pdf

Segmentation and Counting of WBCs and RBCs from Microscopic Blood Sample Images

Blood and its components have an important place in human life and are the best indicator tool in determining many pathological conditions. In particular, the classification of white blood cells is of great importance for the diagnosis of hematological diseases. In this study, 350 microscopic blood smear images were tested with 6 different machine learning algorithms for the classification of white blood cells and their performances were compared. 35 different geometric and statistical (texture) features have been extracted from blood images for training and test parameters of machine learning algorithms. According to the results, the Multinomial Logistic Regression (MLR) algorithm performed better than the other methods with an average 95% test success. The MLR can be used for automatic classification of white blood cells. It can be used especially as a source for diagnosis of diseases for hematologists and internal medicine specialists.

/pdf/classifying-white-blood-cells-using-machine-learning-3zf432csfc.pdf

Classifying White Blood Cells Using Machine Learning Algorithms

This paper presents an automated method for counting red blood cells present in a blood sample. The proposed method addresses the problems of holes present in blood cells and overlapping characteristics of the red blood cells. The procedure is quite simple and straightforward, which utilizes mathematical morphological operations of erosion and dilation for performing different steps. It first thresholds a gray scale image to obtain the binary image using the Otsu thresholding method, and then, performs the hole filling process on the red blood cells if they have holes. Then, the process moves on to the job of counting the red blood cells. For this, each red blood cell is extracted and its shape analysis is performed to decide whether it is circular, non-circular, overlapping or just partially present in the sample. If a cell is only partially present in the image, then it is discarded. In case of overlapping, the number of cells in the overlapped area is determined. Several experimental results have been presented to establish the effectiveness of the method. One of the important findings is that the proposed method gives accurate count of red blood cells of the blood sample, and classifies each cell into one of the four categories mentioned above.

Madhura Ganguly

Papers

An automated method for counting and characterizing red blood cells using mathematical morphology