Does image normalization and intensity resolution impact texture classification?

Kociołek, Marcin; Strzelecki, Michał; Obuchowicz, Rafał

doi:10.1016/j.compmedimag.2020.101716

Cited by 75 publications

(39 citation statements)

References 20 publications

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“…The echotextural variables (mean NPVs and PSDs) were compared using multivariate analysis of variance (ANOVA); a three-way ANOVA was performed to assess the main effects of time (morning vs. evening), scanning order (before or after milking) and scanning plane for each quarter (sagittal vs. coronal within right front vs. right back vs. left front vs. left back udder quarter). In addition, pixel intensities were normalized prior to statistical comparisons of udder echotextural variables by the ±3σ technique [14] to minimize the influence of scanning "noise" and image microartifacts. As there were no statistical differences among the raw and normalized data, the original values were used for ensuing statistical analyses.…”

Section: Statistical Analysesmentioning

confidence: 99%

Associations between Mammary Gland Echotexture and Milk Composition in Cows

Schwarz

Scheeres

Małopolska

et al. 2020

Animals

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Thirty clinically healthy Holstein-Friesian cows underwent twice daily machine milking and ultrasonographic examinations of the udder just prior to and after milking. Digital ultrasonographic images of each udder quarter were subjected to computer-assisted echotextural analyses to obtain mean numerical pixel values (NPVs) and pixel heterogeneity (PSD) of the mammary gland parenchyma. The average milk yield and pH were higher (p < 0.05) in the morning, whereas crude fat, total solids, solids non-fat and citric acid content were higher (p < 0.05) during the evening milking period. Mean NPVs and PSDs of the mammary gland parenchyma were greater (p < 0.05) after than before milking. There were significant correlations among echotextural characteristics of the udder and protein percentage, lactose content and freezing point depression determined in the milk samples collected in the morning and crude protein, casein, lactose and solids non-fat in the evening. Our results can be interpreted to suggest that computerized analysis of the mammary gland ultrasonograms has the makings of a technique for estimating non-fat milk constituents in cows. However, future validating studies are necessary before this method can be employed in commercial settings and research. Moreover, significant inter-quarter differences in udder echogenicity may necessitate further echotextural studies of separate quarters.

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Section: Statistical Analysesmentioning

confidence: 99%

Associations between Mammary Gland Echotexture and Milk Composition in Cows

Schwarz

Scheeres

Małopolska

et al. 2020

Animals

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“…Texture analysis was applied to the second echo images of the Dixon image series (TE = 2.87 ms). To improve the reproducibility and robustness of radiomics features, the voxel intensity range was normalized and quantized to 128 gray levels (31)(32)(33).…”

Section: Image Segmentation and Analysismentioning

confidence: 99%

Combination of Quantitative MRI Fat Fraction and Texture Analysis to Evaluate Spastic Muscles of Children With Cerebral Palsy

et al. 2021

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Background: Cerebral palsy (CP) is the most common cause of physical disability in childhood. Muscle pathologies occur due to spasticity and contractures; therefore, diagnostic imaging to detect pathologies is often required. Imaging has been used to assess torsion or estimate muscle volume, but additional methods for characterizing muscle composition have not thoroughly been investigated. MRI fat fraction (FF) measurement can quantify muscle fat and is often a part of standard imaging in neuromuscular dystrophies. To date, FF has been used to quantify muscle fat and assess function in CP. In this study, we aimed to utilize a radiomics and FF analysis along with the combination of both methods to differentiate affected muscles from healthy ones.Materials and Methods: A total of 9 patients (age range 8–15 years) with CP and 12 healthy controls (age range 9–16 years) were prospectively enrolled (2018–2020) after ethics committee approval. Multi-echo Dixon acquisition of the calf muscles was used for FF calculation. The images of the second echo (TE = 2.87 ms) were used for feature extraction from the soleus, gastrocnemius medialis, and gastrocnemius lateralis muscles. The least absolute shrinkage and selection operator (LASSO) regression was employed for feature selection. RM, FF model (FFM), and combined model (CM) were built for each calf muscle. The receiver operating characteristic (ROC) curve and their respective area under the curve (AUC) values were used to evaluate model performance.Results: In total, the affected legs of 9 CP patients and the dominant legs of 12 healthy controls were analyzed. The performance of RM for soleus, gastrocnemius medialis, and gastrocnemius lateralis (AUC 0.92, 0.92, 0.82, respectively) was better than the FFM (AUC 0.88, 0.85, 0.69, respectively). The combination of both models always had a better performance than RM or FFM (AUC 0.95, 0.93, 0.83). FF was higher in the patient group (FFS 9.1%, FFGM 8.5%, and FFGL 10.2%) than control group (FFS 3.3%, FFGM 4.1%, FFGL 6.6%).Conclusion: The combination of MRI quantitative fat fraction analysis and texture analysis of muscles is a promising tool to evaluate muscle pathologies due to CP in a non-invasive manner.

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“…Yet sometimes due to noise or other factors medical machinery may give an inappropriate or incorrect description. To limit this issue, analyzed regions of interest in the image are often normalized [33].…”

Section: Data Pre-processingmentioning

confidence: 99%

LungAI: A Deep Learning Convolutional Neural Network for Automated Detection of COVID-19 from Posteroanterior Chest X-Rays

Gulati¹

2020

Preprint

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COVID-19 is an infectious disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). As of December 2020, more than 72 million cases have been reported worldwide. The standard method of diagnosis is by Real-Time Reverse Transcription Polymerase Chain Reaction (rRT-PCR) from a Nasopharyngeal Swab. Currently, there is no vaccine or specific antiviral treatment for COVID-19. Due to rate of spreading of the disease manual detection among people is becoming more difficult because of a clear lack of testing capability. Thus there was need of a quick and reliable yet non-labour intensive detection technique. Considering that the virus predominantly appears in the form of a lung based abnormality I made use of Chest X-Rays as our primary mode of detection. For this detection system we made use of Posteroanterior (PA) Chest X-rays of people infected with Bacterial Pneumonia (2780 Images), Viral Pneumonia (1493 Images), Covid-19 (729 Images) as well as those of perfectly Healthy Individuals (1583 Images) procured from various Publicly Available Datasets and Radiological Societies. LungAI is a novel Convolutional Neural Network based on a Hybrid of the DarkNet and AlexNet architecture. The network was trained on 80% of the dataset with 20% kept for validation. The proposed Coronavirus Detection Model performed exceedingly well with an accuracy of 99.16%, along with a Sensitivity value of 99.31% and Specificity value of 99.14%. Thus LungAI has the potential to prove useful in managing the current Pandemic Situation by providing a reliable and fast alternative to Coronavirus Detection given strong results.

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Does image normalization and intensity resolution impact texture classification?

Cited by 75 publications

References 20 publications

Associations between Mammary Gland Echotexture and Milk Composition in Cows

Associations between Mammary Gland Echotexture and Milk Composition in Cows

Combination of Quantitative MRI Fat Fraction and Texture Analysis to Evaluate Spastic Muscles of Children With Cerebral Palsy

LungAI: A Deep Learning Convolutional Neural Network for Automated Detection of COVID-19 from Posteroanterior Chest X-Rays

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