Evaluation of Statistical Features for Medical Image Retrieval

Ruberto, Cecilia Di; Fodde, Giuseppe

doi:10.1007/978-3-642-41181-6_56

Cited by 4 publications

(7 citation statements)

References 15 publications

(15 reference statements)

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“…The statistical feature approach for representing image properties is well-known in image processing [ 41 ]. Extracted statistical features represent the color, texture, or morphological properties of an image.…”

Section: Methodsmentioning

confidence: 99%

Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia

Herzog

Magoulas

2021

Sensors

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Early identification of degenerative processes in the human brain is considered essential for providing proper care and treatment. This may involve detecting structural and functional cerebral changes such as changes in the degree of asymmetry between the left and right hemispheres. Changes can be detected by computational algorithms and used for the early diagnosis of dementia and its stages (amnestic early mild cognitive impairment (EMCI), Alzheimer’s Disease (AD)), and can help to monitor the progress of the disease. In this vein, the paper proposes a data processing pipeline that can be implemented on commodity hardware. It uses features of brain asymmetries, extracted from MRI of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, for the analysis of structural changes, and machine learning classification of the pathology. The experiments provide promising results, distinguishing between subjects with normal cognition (NC) and patients with early or progressive dementia. Supervised machine learning algorithms and convolutional neural networks tested are reaching an accuracy of 92.5% and 75.0% for NC vs. EMCI, and 93.0% and 90.5% for NC vs. AD, respectively. The proposed pipeline offers a promising low-cost alternative for the classification of dementia and can be potentially useful to other brain degenerative disorders that are accompanied by changes in the brain asymmetries.

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Section: Methodsmentioning

confidence: 99%

Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia

Herzog

Magoulas

2021

Sensors

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“…Approaches that are based on statistical features for representing image properties are well-established in image processing [43]. The statistical description of the image texture, color or morphological properties generates a limited number of relevant and distinguishable features.…”

Section: Feature Engineering and Analysismentioning

confidence: 99%

Machine Learning-Supported MRI Analysis of Brain Asymmetry for Early Diagnosis of Dementia

Herzog

Magoulas

2021

Studies in Computational Intelligence

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The chapter focuses on the detection of early degenerative processes in the human brain using computational algorithms and machine learning classification techniques. The research is consistent with the hypothesis that there are changes in brain asymmetry across stages of dementia and Alzheimer's Disease. The proposed approach considers the pattern of changes in the degree of asymmetry between the left and right hemispheres of the brain using structural magnetic resonance imaging of the ADNI database and image analysis techniques. An analysis of levels of asymmetry is performed with the help of statistical features extracted from the segmented asymmetry images. The diagnostic potential of these features is explored using variants of Support Vector Machines and a Convolutional Neural Network. The proposed approach produces very promising results in distinguishing between cognitively normal subjects and patients with early mild cognitive impairment and Alzheimer's Disease, providing evidence that image asymmetry features or MRI images of segmented asymmetry can offer insight on early diagnosis of dementia.

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“…Low‐level features include colour, shape and texture 10 . This work looks into the low‐level textural features, which are the geometric arrangement of the grey levels of image 11 …”

Section: Introductionmentioning

confidence: 99%

“…Statistical textural analysis methods include the Co‐occurrence matrix (second‐level histogram), 3 the histogram features and the Linear binary patterns that look at the local spatial structures 36 . Other textural analysis algorithms exist, such as the grey scale level co‐occurrence matrix, 11 binary Gabor pattern, 37 the local spiking pattern, 38 local binary grey level co‐occurrence matrix (LBGLCM) 39 and the Grey level run‐length matrix (primitive length texture features) 40 . Meenakshi and Gaurav 41 have reviewed other methods thoroughly.…”

Section: Introductionmentioning

confidence: 99%

Transfer learning data adaptation using conflation of low‐level textural features

Wanjiku

Nderu

Kimwele

2022

Engineering Reports

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Adapting the target dataset for a pre‐trained model is still challenging. These adaptation problems result from a lack of adequate transfer of traits from the source dataset; this often leads to poor model performance resulting in trial and error in selecting the best‐performing pre‐trained model. This paper introduces the conflation of source domain low‐level textural features extracted using the first layer of the pre‐trained model. The extracted features are compared to the conflated low‐level features of the target dataset to select a higher‐quality target dataset for improved pre‐trained model performance and adaptation. From comparing the various probability distance metrics, Kullback‐Leibler is adopted to compare the samples from both domains. We experiment on three publicly available datasets and two ImageNet pre‐trained models used in past studies for results comparisons. This proposed approach method yields two categories of the target samples with those with lower Kullback‐Leibler values giving better accuracy, precision and recall. The samples with the lower Kullback‐Leibler values give a higher margin accuracy rate of 0.22%–9.15%, thereby leading to better model adaptation and easier model selection process for the target transfer learning datasets and tasks.

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Evaluation of Statistical Features for Medical Image Retrieval

Cited by 4 publications

References 15 publications

Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia

Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia

Machine Learning-Supported MRI Analysis of Brain Asymmetry for Early Diagnosis of Dementia

Transfer learning data adaptation using conflation of low‐level textural features

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