Towards Large-Scale Histopathological Image Analysis: Hashing-Based Image Retrieval

Abstract

Automatic analysis of histopathological images has been widely utilized leveraging computational image-processing methods and modern machine learning techniques. Both computer-aided diagnosis (CAD) and content-based image-retrieval (CBIR) systems have been successfully developed for diagnosis, disease detection, and decision support in this area. Recently, with the ever-increasing amount of annotated medical data, large-scale and data-driven methods have emerged to offer a promise of bridging the semantic gap between images and diagnostic information. In this paper, we focus on developing scalable image-retrieval techniques to cope intelligently with massive histopathological images. Specifically, we present a supervised kernel hashing technique which leverages a small amount of supervised information in learning to compress a 10 000-dimensional image feature vector into only tens of binary bits with the informative signatures preserved. These binary codes are then indexed into a hash table that enables real-time retrieval of images in a large database. Critically, the supervised information is employed to bridge the semantic gap between low-level image features and high-level diagnostic information. We build a scalable image-retrieval framework based on the supervised hashing technique and validate its performance on several thousand histopathological images acquired from breast microscopic tissues. Extensive evaluations are carried out in terms of image classification (i.e., benign versus actionable categorization) and retrieval tests. Our framework achieves about 88.1% classification accuracy as well as promising time efficiency. For example, the framework can execute around 800 queries in only 0.01 s, comparing favorably with other commonly used dimensionality reduction and feature selection methods.

Text- and Content-Based Medical Image Retrieval in the VISCERAL Retrieval Benchmark

“…In the literature, there are many methods that can automatically extract the visual features to characterize the medical images [2,[26][27][28]. The Bag of Visual Words (BoVW) [29,30] method, which is one of the popular methods for visual content-based image retrieval, is applied as our first content-based retrieval method.…”

An Efficient Approach for Content Based Image Retrieval Using Hierarchical Part-Template and Tree Modeling

“…In [11], a modified block truncation coding presented based on the texture for CBIR system .In this approach the key idea is to get feature vector from the threshold mean over different non-overlapped blocks. In [12], scalable image retrieval approach presented to coordinate smartly with huge histo-pathological images. Here, a supervised kernel hashing approach discussed to reduce the semantic gap between low-level and high-level features.…”

Robust Cell Detection and Segmentation in Histopathological Images Using Sparse Reconstruction and Stacked Denoising Autoencoders

“…Computer-aided diagnosis (CAD) is a promising solution. In CAD, cell detection and segmentation are often prerequisite steps for critical morphological analysis [10,16].…”