M
Mumtaz Hussain Soomro
Researcher at Roma Tre University
Publications - 14
Citations - 229
Mumtaz Hussain Soomro is an academic researcher from Roma Tre University. The author has contributed to research in topics: Image segmentation & Artifact (error). The author has an hindex of 7, co-authored 13 publications receiving 171 citations. Previous affiliations of Mumtaz Hussain Soomro include Petronas & University of Virginia.
Papers
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Proceedings ArticleDOI
Automatic eye-blink artifact removal method based on EMD-CCA
TL;DR: Computational assessment of corrected EEG waveforms reveals that the proposed algorithm retrieves the EEG data by removing the eye blink artifacts reliably and compared to other eye blink artifact removal techniques, the proposed method has two benefits.
Journal ArticleDOI
Haralick's texture features for the prediction of response to therapy in colorectal cancer: a preliminary study
Damiano Caruso,Marta Zerunian,Maria Ciolina,Domenico De Santis,Marco Rengo,Mumtaz Hussain Soomro,Gaetano Giunta,Silvia Conforto,Maurizio Schmid,Emanuele Neri,Andrea Laghi +10 more
TL;DR: Five Haralick’s features showed significant relevance in the prediction of response to therapy in colorectal cancer and might be used as additional imaging biomarker in the oncologic management of coloreCTal patients.
Journal ArticleDOI
Automated Segmentation of Colorectal Tumor in 3D MRI Using 3D Multiscale Densely Connected Convolutional Neural Network
Mumtaz Hussain Soomro,Matteo Coppotelli,Silvia Conforto,Maurizio Schmid,Gaetano Giunta,Lorenzo Del Secco,Emanuele Neri,Damiano Caruso,Marco Rengo,Andrea Laghi +9 more
TL;DR: The proposed CNN architecture, based on densely connected neural network, contains multiscales dense interconnectivity between layers of fine and coarse scales, thus leveraging multiscale contextual information in the network to get better flow of information throughout the network.
Proceedings ArticleDOI
A method for automatic removal of eye blink artifacts from EEG based on EMD-ICA
TL;DR: A new hybrid algorithm that automatically removes the eye blink artifact from the EEG, based on Empirical Mode Decomposition (EMD) and Independent Component Analysis (ICA) is proposed and demonstrates that proposed method recovers the EEG data by removing the eye blinking artifacts reliably.
Journal ArticleDOI
Comparison of Initialization Techniques for the Accurate Extraction of Muscle Synergies from Myoelectric Signals via Nonnegative Matrix Factorization.
TL;DR: Simulation results demonstrate that sparse initialization performs significantly better than all other kinds of initialization in reconstructing muscle synergies, regardless of the correlation level in the data.