Detecting distorted and benign blood cells using the Hough transform based on neural networks and decision trees - Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition - page 385

tion of medical image paterns, they trained two back propagation Artiicial Neural Network
models (3 layers and 4 layers) together with image analysis techniques on morphological fea-
tures of RBCs. The three layers had the best performance with an error of 2.74545e-005 and
86.54% correct recognition rate. The trained three layer ANN acts as a final detection classifier
to determine diseases [7].
In December 2012, D.K. DAS, C. CHAKRABORTY, B. MITRA, A.K. MAITI, and A.K. RAY
introduced a methodology using some techniques of machine learning for characterizing
RBCs in anemia based on microscopic images of peripheral blood smears. In the first, for re-
ducing unevenness of background illumination and noise they preprocessed peripheral blood
smear images based on geometric mean filter and the technique of gray world assumption.
Then watershed segmentation technique applied to erythrocyte cells. The distorted RBCs,
such as, sickle cells, echinocyte, tear drop, acanthocyte, elliptocyte, and benign cells have been
classified as dependent on their morphological shape changes. They observed that when a
small subset of features used by using information gain measures, the logistic regression clas-
siier presented beter in performance. They achieved highest prediction in terms of overall
accuracy by 86.87%, sensitivity to 95.3%, and specificity was 94.13% [ 8 ] .
In May 2013, Thirusitampalam, Hossain, Ghita, and Whelan developed a novel tracking
algorithm that extracted cell motility indicators and determined cellular division (mitosis)
events in large time-lapse phase-contrast image sequences. Their process of automatic, unsu-
pervised cell tracking was carried out in a sequential manner, with the interframe cell's associ-
ation achieved by assessing the variation in the local cellular structures in consecutive frames
from the image sequence. The experimental results indicated that their algorithm achieved
86.10% overall tracking accuracy and 90.12% mitosis detection accuracy [ 9 ] .
Also in May 2013, Khan and Maruf presented an algorithm for cell segmentation and count-
ing via the detection of cell centroids in microscopic images. Their method was speciically
designed for counting circular cells with a high probability of occlusion. The experimental res-
ults showed an accuracy of 92% of cell counting, even at around 60% overlap probability [ 10 ] .
An algorithm presented by Mushabe, Dendere, and Douglas in July 2013 identified and
counted RBCs as well as parasites in order to perform a parasitemia calculation. The authors
employed morphological operations and histogram-based thresholds to detect the RBCs, and
they used boundary curvature calculations and Delaunay triangulation to split overlapped
cells. A Bayesian classifier with their RGB pixel values as features classified the parasites, and
the results showed 98.5% sensitivity and 97.2% specificity for detecting infected RBCs [ 11 ] . In
2014, Rashmi Mukherjee presented an evaluation the morphometric features of placental villi
and capillaries in preeclamptic and normal placentae. The study included light microscopic
images of placental tissue sections of 40 preeclamptic and 35 normotensive pregnant women.
The villi and capillaries characterized based on preprocessing and segmentation of these im-
ages. He applied principal component analysis (PCA), Fisher's linear discriminant analysis
(FLDA), and hierarchical cluster analysis (HCA) to identify placental (morphometric) features,
which are the most significant from microscopic images. He achieved 5 significant morpho-
metric features (>90% overall discrimination accuracy) identified by FLDA, and PCA returned
three most significant principal components cumulatively explained 98.4% of the total vari-
ance [ 12 ] . From this literature survey, it was noticed that research work has been done towards
anemia-afected RBCs' characterization using computer vision approach. The proposed work
methodology has described in Section 6 .