KEYWORDS: Blood, Databases, Pathology, Image retrieval, Image segmentation, Content based image retrieval, Cancer, Medical imaging, RGB color model, Algorithm development
The purpose of this work was to evaluate a newly developed content-based retrieval approach for characterizing a range
of different white blood cells from a database of imaged peripheral blood smears. Specimens were imaged using a 20×
magnification to provide adequate resolution and sufficiently large field of view. The resulting database included a test
ensemble of 96 images (1000×1000 pixels each). In this work, we propose a four-step content-based retrieval method
and evaluate its performance. The content-based image retrieval (CBIR) method starts from white blood cell
identification, followed by three sequential steps including coarse-searching, refined searching, and finally mean-shift
clustering using a hierarchical annular histogram (HAH). The prototype system was shown to reliably retrieve those
candidate images exhibiting the highest-ranked (most similar) characteristics to the query. The results presented here
show that the algorithm was able to parse out subtle staining differences and spatial patterns and distributions for the
entire range of white blood cells under study. Central to the design of the system is that it capitalizes on lessons learned
by our team while observing human experts when they are asked to carry out these same tasks.
In this paper, we propose a novel image classification method based on sparse reconstruction errors to discriminate
cancerous breast tissue microarray (TMA) discs from benign ones. Sparse representation is employed to
reconstruct the samples and separate the benign and cancer discs. The method consists of several steps including
mask generation, dictionary learning, and data classification. Mask generation is performed using multiple scale
texton histogram, integral histogram and AdaBoost. Two separate cancer and benign TMA dictionaries are
learned using K-SVD. Sparse coefficients are calculated using orthogonal matching pursuit (OMP), and the reconstructive
error of each testing sample is recorded. The testing image will be divided into many small patches.
Each small patch will be assigned to the category which produced the smallest reconstruction error. The final
classification of each testing sample is achieved by calculating the total reconstruction errors. Using standard
RGB images, and tested on a dataset with 547 images, we achieved much better results than previous literature.
The binary classification accuracy, sensitivity, and specificity are 88.0%, 90.6%, and 70.5%, respectively.
A performance study was conducted to compare classification accuracy using both multispectral imaging
(MSI) and standard bright-field imaging (RGB) to characterize breast tissue microarrays. The study was
primarily focused on investigating the classification power of texton features for differentiating cancerous
breast TMA discs from normal. The feature extraction algorithm includes two main processes: texton library
training and histogram construction. First, two texton libraries were built for multispectral cubes and RGB
images respectively, which comprised the training process. Second, texton histograms from each
multispectral cube and RGB image were used as testing sets. Finally, within each spectral band, exhaustive
feature selection was used to search for the combination of features that yielded the best classification
accuracy using the pathologic result as a golden standard. Support vector machine was applied as a classifier
using leave-one-out cross-validation. The spectra carrying the greatest discriminatory power were
automatically chosen and a majority vote was used to make the final classification. The study included 122
breast TMA discs that showed poor classification power based on simple visualization of RGB images. Use
of multispectral cubes showed improved sensitivity and specificity compared to the RGB images (85%
sensitivity & 85% specificity for MSI vs. 75% & 65% for RGB). This study demonstrates that use of texton
features derived from MSI datasets achieve better classification accuracy than those derived from RGB
datasets. This study further shows that MSI provided statistically significant improvements in automated
analysis of single-stained bright-field images. Future work will examine MSI performance in assessing multistained
specimens.
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