Probabilistic modeling of Ultrasound Radio- Frequency Time Series Data for Characterizing Cancerous Regions

Ultrasound RF time series data carry a large amount of tissue specific information that can be used in differentiating between cancerous and normal tissues. In this project, the goal is to find tissue specific signature using probabilistic modeling of RF time series data. Hidden Markov Models (HMMs) technique will be used to build a model of cancerous versus normal tissues in patients. HMMs are probabilistic models of linear sequences of observables. They are widely used in the analysis and detection of patterns in time series data. Using HMMs on RF time series can be tackled in different ways. A simplistic approach to the application of HMM is to summarize the information relayed by the RF time series data of cancerous regions of a patient in one model and the normal regions in another model. At each time point, the model shows the probability of getting an observation from different probability distributions with specific means and standard deviations calculated from a training dataset. Figure 3 shows a graphical representation of a sample HMM of RF time series data for a cancerous region.

                                             

       Figure 3 Graphical representation of a sample HMM of RF time series data

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