Feature extraction and classification of blood cells for an automated differential blood count system

2001-07-19
The differential blood counter (DBC) system that we have developed is an attempt to automate the task performed manually by experts in routine. Feature extraction and classification are two important components of our automated system. In this paper, classification of blood cells using various approaches including neural network based classifiers and support vector machine are presented together with the features used in the classification.

Suggestions

Improvement of Hyperspectral Classification Accuracy with Limited Training Data Using Meanshift Segmentation
Özdemir, Okan Bilge; Çetin, Yasemin (2014-04-25)
In this study, the performance of hyperspectral classification algorithms with limited training data investigated. Support Vector Machines (SVM) with Gaussian kernel is used. Principle Component Analysis (PCA) is employed for preprocessing and meanshift segmentation is used to incorporate spatial information with spectral information to observe the effect spatial information. Pattern search algorithm is used to optimize meanshift segmentation parameters. The performance of the algorithm is demonstrated on h...
Disparity disambiguation by fusion of signal- and symbolic-level information
Ralli, Jarno; Diaz, Javier; Kalkan, Sinan; Krueger, Norbert; Ros, Eduardo (Springer Science and Business Media LLC, 2012-01-01)
We describe a method for resolving ambiguities in low-level disparity calculations in a stereo-vision scheme by using a recurrent mechanism that we call signal-symbol loop. Due to the local nature of low-level processing it is not always possible to estimate the correct disparity values produced at this level. Symbolic abstraction of the signal produces robust, high confidence, multimodal image features which can be used to interpret the scene more accurately and therefore disambiguate low-level interpretat...
Computational modeling of electrocardiograms: A finite element approach toward cardiac excitation
Kotikanyadanam, Mohan; Göktepe, Serdar; Kuhl, Ellen (Wiley, 2010-05-01)
The objective of this work is the computational simulation of a patient-specific electrocardiogram (EKG) using a novel, robust, efficient, and modular finite element-based simulation tool for cardiac electrophysiology. We apply a two-variable approach in terms of a fast action potential and a slow recovery variable, whereby the latter phenomenologically summarizes the concentration of ionic currents. The underlying algorithm is based on a staggered solution scheme in which the action potential is introduced...
Rescheduling with controllable processing times for number of disrupted jobs and manufacturing cost objectives
Gürel, Sinan (2015-05-03)
We consider a machine rescheduling problem that arises when a disruption such as machine breakdown occurs to a given schedule. Machine unavailability due to a breakdown requires repairing the schedule as the original schedule becomes infeasible. When repairing a disrupted schedule a desirable goal is to complete each disrupted job on time, i.e. not later than the planned completion time in the original schedule. We consider the case where processing times of jobs are controllable and compressing the process...
Optimisation of dragline inspection intervals with time-counter algorithm
Gölbaşı, Onur; Demirel, Nuray (Informa UK Limited, 2017-01-01)
This research study proposed a novel algorithm, called time-counter, to optimise inspection intervals of production systems. The developed algorithm was applied to two active draglines. The algorithm evaluated random uptime/downtime characteristics of the draglines and the planned dragline halts in the mine. Physical and random non-physical costs were included and annual maintenance cost minimisation was achieved. Dragline-1 and Dragline-2 inspection intervals were optimised as 232 and 184 h and their maint...
Citation Formats
G. ONGUN, U. Halıcı, M. K. Leblebicioğlu, M. V. Atalay, M. Beksac, and S. Beksac, “Feature extraction and classification of blood cells for an automated differential blood count system,” 2001, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/46195.