A computer-aided method based on geometrical texture features for a precocious detection of fetal Hydrocephalus in ultrasound images

Fetal Ultrasound Segmentation and Measurements Using Appearance and Shape Prior Based Density Regression with Deep CNN and Robust Ellipse Fitting

Accurately segmenting the structure of the fetal head (FH) and performing biometry measurements, including head circumference (HC) estimation, stands as a vital requirement for addressing abnormal fetal growth during pregnancy under the expertise of experienced radiologists using ultrasound (US) images. However, accurate segmentation and measurement is a challenging task due to image artifact, incomplete ellipse fitting, and fluctuations due to FH dimensions over different trimesters. Also, it is highly time-consuming due to the absence of specialized features, which leads to low segmentation accuracy. To address these challenging tasks, we propose an automatic density regression approach to incorporate appearance and shape priors into the deep learning-based network model (DR-ASPnet) with robust ellipse fitting using fetal US images. Initially, we employed multiple pre-processing steps to remove unwanted distortions, variable fluctuations, and a clear view of significant features from the US images. Then some form of augmentation operation is applied to increase the diversity of the dataset. Next, we proposed the hierarchical density regression deep convolutional neural network (HDR-DCNN) model, which involves three network models to determine the complex location of FH for accurate segmentation during the training and testing processes. Then, we used post-processing operations using contrast enhancement filtering with a morphological operation model to smooth the region and remove unnecessary artifacts from the segmentation results. After post-processing, we applied the smoothed segmented result to the robust ellipse fitting-based least square (REFLS) method for HC estimation. Experimental results of the DR-ASPnet model obtain 98.86% dice similarity coefficient (DSC) as segmentation accuracy, and it also obtains 1.67 mm absolute distance (AD) as measurement accuracy compared to other state-of-the-art methods. Finally, we achieved a 0.99 correlation coefficient (CC) in estimating the measured and predicted HC values on the HC18 dataset.

An automated cervical cancer diagnosis using genetic algorithm and CANFIS approaches

BACKGROUND

Cervical malignancy is considered among the most perilous cancers affecting women in numerous East African and South Asian nations, both in terms of its prevalence and fatality rates.

OBJECTIVE

This research aims to propose an efficient automated system for the segmentation of cancerous regions in cervical images.

METHODS

The proposed techniques encompass preprocessing, feature extraction with an optimized feature set, classification, and segmentation. The original cervical image undergoes smoothing using the Gaussian Filter technique, followed by the extraction of Local Binary Pattern (LBP) and Grey Level Co-occurrence Matrix (GLCM) features from the enhanced cervical images. LBP features capture pixel relationships within a mask window, while GLCM features quantify energy metrics across all pixels in the images. These features serve to distinguish normal cervical images from abnormal ones. The extracted features are optimized using Genetic Algorithm (GA) as an optimization method, and the optimized sets of features are classified using the Co-Active Adaptive Neuro-Fuzzy Inference System (CANFIS) classification method. Subsequently, a morphological segmentation technique is employed to categorize irregular cervical images, identifying and segmenting malignant regions within them.

RESULTS

The proposed approach achieved a sensitivity of 99.09%, specificity of 99.39%, and accuracy of 99.36%.

CONCLUSION

The proposed approach demonstrated superior performance compared to state-of-the-art techniques, and the results have been validated by expert radiologists.

Multi-apodization with cross-correlation combined with generalized sidelobe canceller applied to ultrasound imaging

BACKGROUND

Beamforming is vital for medical ultrasound imaging systems. The generalized sidelobe canceller (GSC) beamforming can improve the image quality of lateral resolution, but its performance improvement in contrast and robustness is limited.

OBJECTIVE

This paper proposes an improved generalized sidelobe canceller algorithm based on multi-apodization with cross-correlation (MAXB-IGSC), which aims to improve the contrast and robustness of ultrasound imaging while maintaining the high image resolution and background speckle quality of GSC.

METHODS

The proposed MAXB-IGSC uses multiple pairs of complementary received apodization functions to process the echo data individually to obtain multiple pairs of data sets. The average of their normalized cross-correlation coefficients is then calculated and utilized to determine the adaptive subarray length of the GSC covariance matrix and weights the output of the improved GSC.

RESULTS

The MAXB-IGSC improves the contrast ratio (CR) by 171.18% in anechoic cyst simulation and by 91.23%/130.97%/171.76% in geabr_0 (a dataset from the University of Michigan) experiment compared with GSC, respectively. Furthermore, MAXB-IGSC exhibits significant noise immunity, which greatly improves the robustness of the imaging. The technology also maintains the brightness and uniformity of the background speckle.

CONCLUSION

The proposed MAXB-IGSC has potential for obtaining high-quality ultrasound images in clinical applications.

Diagnostic value of multi-parameter MRI and colour B-ultrasound elastography in benign and malignant thyroid nodules

BACKGROUND

The early diagnosis of thyroid cancer depends on the popularisation and development of diagnostic imaging techniques and the continuous improvement of physician diagnosis.

OBJECTIVE

To investigate the clinical value of multi-parameter magnetic resonance imaging (MRI) and colour B-ultrasound elastography in thyroid nodules.

METHODS

The clinical and imaging data of 252 patients with thyroid nodules who were admitted to our hospital were collected. All patients underwent preoperative colour B-ultrasound elastography and MRI. The postoperative pathological results were the gold standard for diagnosing benign and malignant thyroid nodules. The accuracy, sensitivity and specificity of MRI, colour B-ultrasound elastography and their combination for diagnosing benign and malignant thyroid nodules were compared.

RESULTS

This study included 252 patients with 388 nodules. There were 169 patients with solitary nodules and 83 patients with multiple nodules. The maximum diameter of the thyroid nodules was 0.32-1.00 (0.75 ± 0.20) cm. The accuracy of MRI diagnosis (348/388) was 89.69%, the sensitivity was 92.98%, and the specificity was 65.22%. The diagnostic accuracy, sensitivity and specificity of colour B-ultrasound elastography (332/388) were 85.57%, 88.30% and 65.22%, respectively. The accuracy rate of combined diagnosis (376/388) was 96.91%, the sensitivity was 98.25%, and the specificity was 86.96%, which was significantly higher than MRI and colour B-ultrasound elastography alone. The area under the curve (AUC) of MRI, colour B-ultrasound elastography and combined diagnosis were 0.768, 0.791 and 0.926, respectively. The AUC of the three diagnostic methods was > 0.7, indicating that the three diagnostic methods had good diagnostic value. The AUC for combined diagnosis was significantly higher than that of MRI and colour B-mode ultrasound elastography alone.

CONCLUSION

Combined ultrasound and MRI have high diagnostic accuracy and specificity for benign and malignant thyroid nodules. This diagnostic method can be applied in clinical practice.

Hybrid clustering system using Nystagmus parameters discrimination for vestibular disorder diagnosis

BACKGROUD AND OBJECTIVE

The control of clinical manifestation of vestibular system relies on an optimal diagnosis. This study aims to develop and test a new automated diagnostic scheme for vestibular disorder recognition.

METHODS

In this study we stratify the Ellipse-fitting technique using the Video Nysta Gmographic (VNG) sequence to obtain the segmented pupil region. Furthermore, the proposed methodology enabled us to select the most optimum VNG features to effectively conduct quantitative evaluation of nystagmus signal. The proposed scheme using a multilayer neural network classifier (MNN) was tested using a dataset involving 98 patients affected by VD and 41 normal subjects.

RESULTS

The new MNN scheme uses only five temporal and frequency parameters selected out of initial thirteen parameters. The scheme generated results reached 94% of classification accuracy.

CONCLUSIONS

The developed expert system is promising in solving the problem of VNG analysis and achieving accurate results of vestibular disorder recognition or diagnosis comparing to other methods or classifiers.