Pixel N-grams for mammographic lesion classification
01 Jan 2017-pp 107-111
TL;DR: A novel Pixel N- gram approach inspired from character N-grams in the text retrieval context has been applied for mammographic lesion classification and results show that optimum value of N is equal to 3 and MLP classifier performs better than SVM and KNN classifier using 3-gram features.
Abstract: Automated classification algorithms have been applied to breast cancer diagnosis in order to improve the diagnostic accuracy and turnover time. However, classification accuracy, sensitivity and specificity could still be improved further. Moreover, reducing computational cost is another challenge as the number of images to be analyzed is typically large. In this paper, a novel Pixel N-gram approach inspired from character N-grams in the text retrieval context has been applied for mammographic lesion classification. The experiments on real world database demonstrate that the Pixel N-grams outperform the existing histogram as well as Haralick features with respect to classification accuracy as well as sensitivity. Effect of varying N and using various classifiers is also analyzed in this paper. Results show that optimum value of N is equal to 3 and MLP classifier performs better than SVM and KNN classifier using 3-gram features.
Citations
More filters
29 Jan 2019
TL;DR: An explanation for the relative poor performance of Pixel N-grams with diabetic retinopathy that draws on concepts associated with the No Free Lunch theorem are presented.
Abstract: The extraction of Bag of Visual Words (BoVW) features from retinal images for automated classification has been shown to be effective but computationally expensive. Histogram and co-variance matrix features do not generally result in models that have the same predictive accuracy as BoVW and are still computationally expensive. The discovery of features that result in accurate image classification on computationally constrained devices such as smartphones would enable new and promising applications for image classification. For example, smartphone retinal cameras can conceivably make diabetic retinopathy widely available and potentially reduce undiagnosed retinopathy if it could be achieved with computationally simple classification algorithms. A novel image feature extraction technique inspired by N-grams in text mining, called 'Pixel N-grams' is described that can serve this purpose. Results on mammogram and texture classification have shown high accuracy despite the reduced computational complexity. However retinal scan classification results using Pixel N-grams lag behind BoVW approaches. An explanation for the relative poor performance of Pixel N-grams with diabetic retinopathy that draws on concepts associated with the No Free Lunch theorem are presented.
1Â citations
References
More filters
01 Nov 1973
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Abstract: Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on gray-tone spatial dependancies, and illustrates their application in category-identification tasks of three different kinds of image data: photomicrographs of five kinds of sandstones, 1:20 000 panchromatic aerial photographs of eight land-use categories, and Earth Resources Technology Satellite (ERTS) multispecial imagery containing seven land-use categories. We use two kinds of decision rules: one for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89 percent for the photomicrographs, 82 percent for the aerial photographic imagery, and 83 percent for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.
20,442Â citations
"Pixel N-grams for mammographic lesi..." refers methods in this paper
...Therefore, 3-gram features are used to compare the performance with other existing techniques such as histogram and Haralick features [25]....
[...]
01 Mar 2009
TL;DR: An overview of recent advances in the development of CAD systems and related techniques for breast cancer detection and diagnosis focuses on key CAD techniques developed recently, including detection of calcifications, detection of masses, Detection of architectural distortion, detectionof bilateral asymmetry, image enhancement, and image retrieval.
Abstract: Breast cancer is the second-most common and leading cause of cancer death among women. It has become a major health issue in the world over the past 50 years, and its incidence has increased in recent years. Early detection is an effective way to diagnose and manage breast cancer. Computer-aided detection or diagnosis (CAD) systems can play a key role in the early detection of breast cancer and can reduce the death rate among women with breast cancer. The purpose of this paper is to provide an overview of recent advances in the development of CAD systems and related techniques. We begin with a brief introduction to some basic concepts related to breast cancer detection and diagnosis. We then focus on key CAD techniques developed recently for breast cancer, including detection of calcifications, detection of masses, detection of architectural distortion, detection of bilateral asymmetry, image enhancement, and image retrieval.
564Â citations
"Pixel N-grams for mammographic lesi..." refers methods in this paper
...Diagnoses made with the help of computerized medical image analysis tools is known as computer aided diagnosis (CAD) [6]....
[...]
488Â citations
TL;DR: It is demonstrated empirically how overlapping character n-gram tokenization can provide retrieval accuracy that rivals the best current language-specific approaches for European languages and is a good choice for those languages, and the increased storage and time requirements of the technique.
Abstract: The Cross-Language Evaluation Forum has encouraged research in text retrieval methods for numerous European languages and has developed durable test suites that allow language-specific techniques to be investigated and compared. The labor associated with crafting a retrieval system that takes advantage of sophisticated linguistic methods is daunting. We examine whether language-neutral methods can achieve accuracy comparable to language-specific methods with less concomitant software complexity. Using the CLEF 2002 test set we demonstrate empirically how overlapping character n-gram tokenization can provide retrieval accuracy that rivals the best current language-specific approaches for European languages. We show that n e 4 is a good choice for those languages, and document the increased storage and time requirements of the technique. We report on the benefits of and challenges posed by n-grams, and explain peculiarities attendant to bilingual retrieval. Our findings demonstrate clearly that accuracy using n-gram indexing rivals or exceeds accuracy using unnormalized words, for both monolingual and bilingual retrieval.
356Â citations
"Pixel N-grams for mammographic lesi..." refers methods in this paper
...The N-gram model had proven to be more accurate than other models in text context [11]....
[...]
TL;DR: The presented BCSC outcomes data and performance benchmarks may be used by mammography facilities and individual radiologists to evaluate their own performance for diagnostic mammography as determined by means of periodic comprehensive audits.
Abstract: PURPOSE: To evaluate a range of performance parameters pertinent to the comprehensive auditing of diagnostic mammography examinations, and to derive performance benchmarks therefrom, by pooling data collected from large numbers of patients and radiologists that are likely to be representative of mammography practice in the United States MATERIALS AND METHODS: Institutional review board approval was met, informed consent was not required, and this study was Health Insurance Portability and Accountability Act compliant Six mammography registries contributed data to the Breast Cancer Surveillance Consortium (BCSC), providing patient demographic and clinical information, mammogram interpretation data, and biopsy results from defined population-based catchment areas The study involved 151 mammography facilities and 646 interpreting radiologists The study population included women 18 years of age or older who underwent at least one diagnostic mammography examination between 1996 and 2001 Collected data wer
191Â citations
"Pixel N-grams for mammographic lesi..." refers background in this paper
...However, interpretation of lesions in mammograms is a very difficult and time consuming task for the radiologists as the features of the abnormality are obscured or can be similar to those of the normal tissue [4]....
[...]