BRISC

Overview

BRISC provides a framework for texture feature extraction and similarity comparison of computed tomography (CT) lung nodule images. It was written in C# .NET 2.0 using Visual Studio .NET 2005 and is designed to be functional and extensible.

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Description

Here is a description of the project from our SPIE abstract:

In this paper we will present a content-based image retrieval (CBIR) system for a database of pulmonary nodule images, with a comparison of the effectiveness of various texture features and similarity measures in retrieving similar images from a medical database. We are particularly interested in how well texture feature analysis performs with lung nodules obtained from the Lung Image Database Consortium (LIDC).

The LIDC provided a set of lung CT images along with information about nodules shown in these images. In our paper we will compare three different types of texture features: (1) Co-occurrence matrices, (2) Gabor filters, and (3) Markov random fields. These methods are used to extract a “feature vector” (a series of numbers) from images that represent the image’s signature. This vector is then compared with the vectors of other images by various similarity measures.

We have decided to base our evaluation on the idea that the first results returned by the system for a particular nodule should be other instances of that same nodule, perhaps on a different CT slice or marked and rated by a different radiologist. Thus, ground truth is determined by objective, a priori knowledge about the nodules. In this way, precision is defined as the number of retrieved instances of the query nodule divided by the number of retrieved images and recall is defined as the number of retrieved instances of the query nodule divided by the number of total instances of the query nodule. We have determined that Gabor-based image features generally perform better than global co-occurrence measures for the images in the LIDC database, with a maximum average precision of 68%.

Features

The nodule viewer is currently capable of the following:

  • Viewing all nodules and their original DICOM images
  • Calculating Haralick statistics and Gabor responses on segmented DICOM images
  • Nodule retrieval based on Haralick descriptors, with the option to customize the feature vector used
  • Nodule retrieval based on Gabor responses, with options for point- or histogram-based similarity measures
  • Limit responses by number (top N items) or distance thresholding
  • Perform on-the-fly DICOM window level contrast enhancement

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