Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The paper proposes a new shape morphometry approach that combines advanced classification techniques with geometric features to identify morphological abnormalities on the brain surface. Our aim is to improve the classification accuracy in distinguishing beten normal subjects and schizophrenic patients. The approach is inspired by natural language processing. Local brain surface geometric patterns are quantized to visual words, and their co-occurrences are encoded as visual topic. To do this, a generative model, the probabilistic Latent Semantic Analysis is learned from quantized shape descriptors (visual words). Finally,  extract from the learned models a generative score, that is used as input of a Support Vector Machine (SVM), defining an hybrid generative/discriminative classification algorithm. An exhaustive experimental section is proposed on a dataset consisting of MRI scans from 64 patients and 60 control subjects. Promising results are reporting by observing accuracies up to 86.13%.
['Umberto Castellani', 'Alessandro Perina', 'Vittorio Murino', 'Marcella Bellani', 'Gianluca Rambaldelli', 'Michele Tansella', 'Paolo Brambilla']
I am writing a research paper on a idea Brain morphometry by probabilistic latent semantic analysis
[{"from": "human", "value": "I am writing a research paper on a idea Brain morphometry by probabilistic latent semantic analysis"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The paper proposes a new shape morphometry approach that combines advanced classification techniques with geometric features to identify morphological abnormalities on the brain surface. Our aim is to improve the classification accuracy in distinguishing beten normal subjects and schizophrenic patients. The approach is inspired by natural language processing. Local brain surface geometric patterns are quantized to visual words, and their co-occurrences are encoded as visual topic. To do this, a generative model, the probabilistic Latent Semantic Analysis is learned from quantized shape descriptors (visual words). Finally,  extract from the learned models a generative score, that is used as input of a Support Vector Machine (SVM), defining an hybrid generative/discriminative classification algorithm. An exhaustive experimental section is proposed on a dataset consisting of MRI scans from 64 patients and 60 control subjects. Promising results are reporting by observing accuracies up to 86.13%."}]