Invariant Moments, Textural and Deep Features for Diagnostic MR and CT Image Retrieval

Putzu, Lorenzo; Loddo, Andrea; Ruberto, Cecilia Di

doi:10.1007/978-3-030-89128-2_28

Cited by 8 publications

(4 citation statements)

References 32 publications

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“…This aspect has been verified with deep features. In contrast, the features extracted with moments are highly correlated with each other, as can be seen in the work of Putzu et al [ 89 ], where it is highlighted that feature selection applied to moments does not bring any benefit, unlike other categories of descriptors. This aspect provides additional robustness to their performance.…”

Section: Experimental Resultsmentioning

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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Section: Experimental Resultsmentioning

confidence: 99%

On The Potential of Image Moments for Medical Diagnosis

2023

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“…HC features encompass diverse techniques and methodologies to extract morphological, pixellevel, and textural information from images. These features can be categorized into three primary categories: invariant moments, textural features, and color-based features [30]. Each category is briefly described below, while every parameter has been set by considering approaches in similar contexts [10].…”

Section: Handcrafted Featuresmentioning

confidence: 99%

An Anomaly Detection Approach to Determine Optimal Cutting Time in Cheese Formation

Loddo,

Ghiani,

Perniciano

et al. 2024

Preprint

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The production of cheese, a beloved culinary delight worldwide, faces challenges in maintaining consistent product quality and operational efficiency. One crucial stage in this process is determining the precise cutting time during curd formation, which significantly impacts the quality of the cheese. Misjudging this timing can lead to the production of inferior products, harming a company's reputation and revenue. Conventional methods often fall short of accurately assessing variations in coagulation conditions due to the inherent potential for human error. To address this issue, we propose an anomaly detection-based approach. In this approach, we treat the class representing curd formation as the anomaly to be identified. Our proposed solution involves utilizing a one-class, fully convolutional data description network, which we compared against several state-of-the-art methods to detect deviations from the standard coagulation patterns. Encouragingly, our results show F1 scores of up to 0.92, indicating the effectiveness of our approach.

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“…HC features encompass diverse techniques and methodologies to extract morphological, pixel-level, and textural information from images. These features can be categorized into three primary categories: invariant moments, textural features, and color-based features [55]. Each category is briefly described below, while every parameter has been set by considering approaches in similar contexts [11].…”

Section: Handcrafted Featuresmentioning

confidence: 99%

An Anomaly Detection Approach to Determine Optimal Cutting Time in Cheese Formation

Loddo,

Ghiani,

Perniciano

et al. 2024

Information

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View full text Add to dashboard Cite

The production of cheese, a beloved culinary delight worldwide, faces challenges in maintaining consistent product quality and operational efficiency. One crucial stage in this process is determining the precise cutting time during curd formation, which significantly impacts the quality of the cheese. Misjudging this timing can lead to the production of inferior products, harming a company’s reputation and revenue. Conventional methods often fall short of accurately assessing variations in coagulation conditions due to the inherent potential for human error. To address this issue, we propose an anomaly-detection-based approach. In this approach, we treat the class representing curd formation as the anomaly to be identified. Our proposed solution involves utilizing a one-class, fully convolutional data description network, which we compared against several state-of-the-art methods to detect deviations from the standard coagulation patterns. Encouragingly, our results show F1 scores of up to 0.92, indicating the effectiveness of our approach.

show abstract

Invariant Moments, Textural and Deep Features for Diagnostic MR and CT Image Retrieval

Cited by 8 publications

References 32 publications

On The Potential of Image Moments for Medical Diagnosis

On The Potential of Image Moments for Medical Diagnosis

An Anomaly Detection Approach to Determine Optimal Cutting Time in Cheese Formation

An Anomaly Detection Approach to Determine Optimal Cutting Time in Cheese Formation

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