Pallavi Vajinepalli

Automatic doppler signal analysis to assess utero-placental circulation for identifying high risk pregnancies

High risk pregnancy conditions such as preeclampsia, pregnancy induced hypertension, intra-uterine growth restriction and gestational diabetes are associated with defective utero-placental circulation. These conditions, if undetected early during pregnancy, are associated with poor pregnancy outcomes including high morbidity/mortality for the fetus/mother. The current state of the art for monitoring such conditions is via (color) Doppler ultrasound with key clinical parameters being observed in uterine and umbilical arteries being the resistance index (RI), pulsatility index (PI) and AB index and early diastolic notching. High risk conditions in pregnancy manifest as abnormal flow profiles and indices in select fetal/maternal blood vessels. These parameters are gold standard as far as current clinical practice goes but they still suffer from low sensitivity in detecting and predicting the above mentioned high risk conditions at an earlier stage. In this paper, we propose a method based on Doppler signal analysis that automatically identifies the above conditions with higher sensitivity even when the current RI/PI indices are normal.

Dynamic illumination based system to remove the glare and improve the quality of medical images

Medical images taken from camera based devices (e.g. laparoscope, colposcope, retinoscope, etc) are greatly affected by numerous bright reflection spots (called glare or specular reflections). This may affect the visibility of the abnormal features (if present in the glare locations). We have developed a novel solution to overcome this problem by incorporating a multi-LED lighting solution. This will intelligently and rapidly switch on and off the LEDs in a pattern that dynamically and geometrically shifts/shuffles these glare spots back and forth in the image such that every glare-affected area of a single image frame can be reconstructed from a few adjacent time-frame images. We have built the prototype that successfully demonstrates how the glare problem in the medical video/image can be satisfactorily solved, significantly enhancing the accuracy of this vital procedure in the diagnosis of diseases. We achieve 65-95% reduction in specularity on phantom model using the proposed approach.

Automated fetal spine detection in ultrasound images

A novel method is proposed for the automatic detection of fetal spine in ultrasound images along with its orientation in this paper. This problem presents a variety of challenges, including robustness to speckle noise, variations in the visible shape of the spine due to orientation of the ultrasound probe with respect to the fetus and the lack of a proper edge enclosing the entire spine on account of its composition out of distinct vertebra. The proposed method improves robustness and accuracy by making use of two independent techniques to estimate the spine, and then detects the exact location using a cross-correlation approach. Experimental results show that the proposed method is promising for fetal spine detection.