Statistical measurement of ultrasound placenta images using segmentation approach

Abstract

Medical diagnosis is the major challenge faced by the medical experts. Highly specialized tools are necessary to assist the experts in diagnosing the diseases. Gestational Diabetes Mellitus is a condition in pregnant women which increases the blood sugar levels. It complicates the pregnancy by affecting the placental growth. The ultrasound screening of placenta in the initial stages of gestation helps to identify the complication induced by GDM on the placental development which accounts for the fetal growth. This work focus on the classification of ultrasound placenta images into normal and abnormal images based on statistical measurements. The ultrasound images are usually low in resolution which may lead to loss of characteristic features of the ultrasound images. The placenta images obtained in an ultrasound examination is stereo mapped to reconstruct the placenta structure from the ultrasound images. The dimensionality reduction is done on stereo mapped placenta images using wavelet decomposition. The ultrasound placenta image is segmented using watershed approach to obtain the statistical measurements of the stereo mapped placenta images. Using the statistical measurements, the ultrasound placenta images are then classified as normal and abnormal using Back Propagation neural networks.

Level-base compounded logarithmic curve function for colour image enhancement

“…Gestational Diabetes Mellitus complicates pregnancy by affecting placental growth. Malathi and Shanti [22] used a watershed approach to obtain the statistical measurements of the stereo mapped placenta images after the dimensionality of those images was reduced by wavelet decomposition. The images were then classified into normal and abnormal images by Back Propagation neural networks.…”

AI-PLAX: AI-based placental assessment and examination using photos

“…Because we cannot directly capture a photo for the placenta under visible light spectrum prior to the delivery, pre-delivery placental imaging research has been focused on images obtained through other means, e.g. , MRI ( Alansary et al, 2016 ) and ultrasound ( Malathi and Shanthi, 2011 , Looney et al, 2017 ). Pre-delivery placental imaging research focuses on segmentation, which can be used as visual aids for doctors.…”

Magnetic Resonance Super-resolution Imaging Measurement with Dictionary-optimized Sparse Learning

“…Sparse representation-based super-resolution reconstruction is a recently proposed method for image recovery. The sparse representation model-based image processing performs well on image denoising [1], image deblurring [2], [3], image restoration [4]. The sparse representation methods include rapid sparse representation [5], dictionary-based learning method, for example KSVD [6], MOD [7], pixel selectionbased sparse representation [8], locality constrained sparse representation [9], precise dictionary representation [10], dictionary selection-based sparse representation [11].…”