Comparison of Statistical Features for Medical Colour Image Classification

Ruberto, Cecilia Di; Fodde, Giuseppe; Putzu, Lorenzo

doi:10.1007/978-3-319-20904-3_1

Cited by 12 publications

(6 citation statements)

References 25 publications

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“…Although these diseases are not usual, they have aroused medical interest in recent years. For that reason, this dataset is one of the most popular databases because it has been used on several studies for classification [49,50] and registration [51]. The whole image dataset is stained with Hematoxylin/Eosin (H+E).…”

Section: Lymphomamentioning

confidence: 99%

A Comparative Study on Discrete Shmaliy Moments and Their Texture-Based Applications

González

Nava

Escalante-Ramı́rez

2018

Mathematical Problems in Engineering

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In recent years, discrete orthogonal moments have attracted the attention of the scientific community because they are a suitable tool for feature extraction. However, the numerical instability that arises because of the computation of high-order moments is the main drawback that limits their wider application. In this article, we propose an image classification method that avoids numerical errors based on discrete Shmaliy moments, which are a new family of moments derived from Shmaliy polynomials. Shmaliy polynomials have two important characteristics: one-parameter definition that implies a simpler definition than popular polynomial bases such as Krawtchouk, Hahn, and Racah; a linear weight function that eases the computation of the polynomial coefficients. We use IICBU-2008 database to validate our proposal and include Tchebichef and Krawtchouk moments for comparison purposes. The experiments are carried out through 5-fold cross-validation, and the results are computed using random forest, support vector machines, naïve Bayes, and k-nearest neighbors classifiers.

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Section: Lymphomamentioning

confidence: 99%

A Comparative Study on Discrete Shmaliy Moments and Their Texture-Based Applications

González

Nava

Escalante-Ramı́rez

2018

Mathematical Problems in Engineering

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“…At the same time, there are also many different approaches to analyse a texture. Statistical methods define textures as stochastic processes and characterise them by a few statistical features [11]. The most relevant statistical approach is still the co‐occurrence matrix [3].…”

Section: On Texton and Texture Orientationmentioning

confidence: 99%

Histogram of Radon transform and texton matrix for texture analysis and classification

Ruberto

2017

IET Image Processing

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“…In this study, we focused on methods for direct image classification, which consists of two main steps: feature extraction and classification. The first step generally consists of extracting a set of parameters from an image to characterise the shapes, colour, and texture it contains [ 6 ]. In the second step, the extracted features are used to build a model of known cases (during the training phase) and provide a diagnosis of unknown cases (during the testing phase).…”

Section: Introductionmentioning

confidence: 99%

On The Potential of Image Moments for Medical Diagnosis

2023

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Medical imaging is widely used for diagnosis and postoperative or post-therapy monitoring. The ever-increasing number of images produced has encouraged the introduction of automated methods to assist doctors or pathologists. In recent years, especially after the advent of convolutional neural networks, many researchers have focused on this approach, considering it to be the only method for diagnosis since it can perform a direct classification of images. However, many diagnostic systems still rely on handcrafted features to improve interpretability and limit resource consumption. In this work, we focused our efforts on orthogonal moments, first by providing an overview and taxonomy of their macrocategories and then by analysing their classification performance on very different medical tasks represented by four public benchmark data sets. The results confirmed that convolutional neural networks achieved excellent performance on all tasks. Despite being composed of much fewer features than those extracted by the networks, orthogonal moments proved to be competitive with them, showing comparable and, in some cases, better performance. In addition, Cartesian and harmonic categories provided a very low standard deviation, proving their robustness in medical diagnostic tasks. We strongly believe that the integration of the studied orthogonal moments can lead to more robust and reliable diagnostic systems, considering the performance obtained and the low variation of the results. Finally, since they have been shown to be effective on both magnetic resonance and computed tomography images, they can be easily extended to other imaging techniques.

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Comparison of Statistical Features for Medical Colour Image Classification

Cited by 12 publications

References 25 publications

A Comparative Study on Discrete Shmaliy Moments and Their Texture-Based Applications

A Comparative Study on Discrete Shmaliy Moments and Their Texture-Based Applications

Histogram of Radon transform and texton matrix for texture analysis and classification

On The Potential of Image Moments for Medical Diagnosis

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