Dr. Alfredo Illanes
Short Bio
I am a senior researcher in the Virtual and Augmented Reality Group at the Research Campus STIMULATE, Otto von Guericke University Magdeburg. I received my degree in Electronic Engineering from Universidad Federico Santa María in Chile, a Master’s degree in Digital Signal Processing from Université de Nice Sophia Antipolis, and a PhD in Biosignal Processing and Modeling from INRIA Rennes in France.
Before joining the VAR group, I worked as an assistant professor in Chile, where I was responsible for teaching in digital signal processing, advanced signal processing, machine learning, and dynamical modeling. My research at that time focused on signal and image processing for vibratory and biomedical processes. I later co-founded two technology startups in France, Chile, and Germany, developing applied solutions in computer vision and vibroacoustic medical guidance.
My current work concentrates on vibroacoustic sensing and signal processing for minimally invasive surgery, with a focus on tool–tissue interaction monitoring, surgical digitalization, and data-driven methods based on vibratory and acoustic information.
Find me also here: 
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ORCID | 
ResearchGate | NCBI Bibliography
Research Interests
My research focuses on advanced signal and image processing methods for medical applications. Current topics include:
- Vibroacoustic monitoring of tool–tissue interactions in minimally invasive procedures
- Digitalization of surgical workflows using vibratory and acoustic sensing
- Time-frequency analysis, parametrical modeling, and machine learning for biomedical and vibratory processes
- Audio data mining and acoustic characterization for medical decision support
Publications
2025
Urrutia, R; Espejo, D; Guerra, M; Vio, K; Sühn, T; Esmaeili, N; Boese, A; Fuentealba, P; Illanes, A; Hansen, C; Poblete, V
Exploring Deep Clustering Methods in Vibro-Acoustic Sensing for Enhancing Biological Tissue Characterization Journal Article
In: IEEE Access, vol. 13, pp. 80395–80406, 2025, ISSN: 2169-3536.
@article{urrutia_exploring_2025,
title = {Exploring Deep Clustering Methods in Vibro-Acoustic Sensing for Enhancing Biological Tissue Characterization},
author = {R Urrutia and D Espejo and M Guerra and K Vio and T Sühn and N Esmaeili and A Boese and P Fuentealba and A Illanes and C Hansen and V Poblete},
url = {https://ieeexplore.ieee.org/document/10981752/},
doi = {10.1109/ACCESS.2025.3566280},
issn = {2169-3536},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {IEEE Access},
volume = {13},
pages = {80395–80406},
abstract = {Nonlinear dimensionality reduction techniques, often referred to as manifold learning, are increasingly valuable for data visualization and unsupervised clustering. In the context of surgery and medicine, these methods facilitate the analysis of complex datasets, enabling pattern recognition in surgical data. This study explores the characterization of six tissue types through manifold learning and unsupervised clustering, utilizing vibro-acoustic (VA) signals collected from manual palpation experiments. A wireless sensor mounted at the tip of a surgical instrument was used to acquire 1,680 VA signals, which were processed using Fourier transform and cepstral analysis for feature extraction. We assessed the performance of two dimensionality reduction techniques: uniform manifold approximation and projection (UMAP) and variational autoencoder (VAE). Results indicate that cepstral features combined with UMAP yield superior clustering performance compared to VAE, achieving higher classification accuracy ( 92 % vs. 87 % ) and better-defined clusters with greater compactness. The observed differences in performance are linked to the intrinsic properties of the tissues, particularly surface characteristics such as friction and moisture, which affect signal consistency. Additionally, we compared our approach with previous works, including a study utilizing the same dataset, where our methodology demonstrated improved accuracy. Future research will focus on refining the VAE model, increasing the diversity of tissue samples, and validating the proposed approach in real surgical settings to enhance its applicability in minimally invasive surgery.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Nonlinear dimensionality reduction techniques, often referred to as manifold learning, are increasingly valuable for data visualization and unsupervised clustering. In the context of surgery and medicine, these methods facilitate the analysis of complex datasets, enabling pattern recognition in surgical data. This study explores the characterization of six tissue types through manifold learning and unsupervised clustering, utilizing vibro-acoustic (VA) signals collected from manual palpation experiments. A wireless sensor mounted at the tip of a surgical instrument was used to acquire 1,680 VA signals, which were processed using Fourier transform and cepstral analysis for feature extraction. We assessed the performance of two dimensionality reduction techniques: uniform manifold approximation and projection (UMAP) and variational autoencoder (VAE). Results indicate that cepstral features combined with UMAP yield superior clustering performance compared to VAE, achieving higher classification accuracy ( 92 % vs. 87 % ) and better-defined clusters with greater compactness. The observed differences in performance are linked to the intrinsic properties of the tissues, particularly surface characteristics such as friction and moisture, which affect signal consistency. Additionally, we compared our approach with previous works, including a study utilizing the same dataset, where our methodology demonstrated improved accuracy. Future research will focus on refining the VAE model, increasing the diversity of tissue samples, and validating the proposed approach in real surgical settings to enhance its applicability in minimally invasive surgery.
Urrutia, R; Ayman, F; Boese, A; Hansen, C; Illanes, A
Needle Puncture Detection Using Vibroacoustic Sensing in Layered Phantoms Journal Article
In: 2025.
@article{urrutia_needle_2025,
title = {Needle Puncture Detection Using Vibroacoustic Sensing in Layered Phantoms},
author = {R Urrutia and F Ayman and A Boese and C Hansen and A Illanes},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
keywords = {},
pubstate = {published},
tppubtype = {article}
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2024
Urrutia, R; Espejo, D; Sühn, T; Guerra, M; Fuentealba, P; Poblete, V; Boese, A; Illanes, A
Variational Autoencoder feature clustering for tissue classification in robotic palpation. Journal Article
In: Current Directions in Biomedical Engineering, vol. 10, iss. 1, pp. 89, 2024, ISSN: 2364-5504.
@article{urrutia2024variational,
title = {Variational Autoencoder feature clustering for tissue classification in robotic palpation.},
author = {R Urrutia and D Espejo and T Sühn and M Guerra and P Fuentealba and V Poblete and A Boese and A Illanes},
doi = {10.1515/cdbme-2024-0123},
issn = {2364-5504},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Current Directions in Biomedical Engineering},
volume = {10},
issue = {1},
pages = {89},
abstract = {Minimally Invasive Robotic Surgery (MIRS) has emerged as a transformative approach in surgical practice, offering reduced patient trauma and enhanced precision. However, challenges persist, including the loss of tactile feedback for surgeons. This study explores the application of machine learning algorithms, specifically variational autoencoders, in vibro-acoustic (VA) signal analysis to address this issue. Our comparative analysis evaluates the potential of supervised learning in surgical data analysis, contributing to advancements in surgical technology. Despite achieving an accuracy of 81%, our results indicate opportunities for further refinement, considering the superior accuracies reported in previous studies. This research underscores the importance of innovative approaches in medical data analysis for optimizing patient care in minimally invasive surgery.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Minimally Invasive Robotic Surgery (MIRS) has emerged as a transformative approach in surgical practice, offering reduced patient trauma and enhanced precision. However, challenges persist, including the loss of tactile feedback for surgeons. This study explores the application of machine learning algorithms, specifically variational autoencoders, in vibro-acoustic (VA) signal analysis to address this issue. Our comparative analysis evaluates the potential of supervised learning in surgical data analysis, contributing to advancements in surgical technology. Despite achieving an accuracy of 81%, our results indicate opportunities for further refinement, considering the superior accuracies reported in previous studies. This research underscores the importance of innovative approaches in medical data analysis for optimizing patient care in minimally invasive surgery.
2023
Urrutia, R; Espejo, D; Evens, N; Guerra, M; Sühn, T; Boese, A; Hansen, C; Fuentealba, P; Illanes, A; Poblete, V
Clustering Methods for Vibro-Acoustic Sensing Features as a Potential Approach to Tissue Characterisation in Robot-Assisted Interventions Journal Article
In: Sensors, vol. 23, no. 23, pp. 9297, 2023, ISSN: 1424-8220, (Publisher: Multidisciplinary Digital Publishing Institute).
@article{urrutia_clustering_2023,
title = {Clustering Methods for Vibro-Acoustic Sensing Features as a Potential Approach to Tissue Characterisation in Robot-Assisted Interventions},
author = {R Urrutia and D Espejo and N Evens and M Guerra and T Sühn and A Boese and C Hansen and P Fuentealba and A Illanes and V Poblete},
url = {https://www.mdpi.com/1424-8220/23/23/9297},
doi = {10.3390/s23239297},
issn = {1424-8220},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Sensors},
volume = {23},
number = {23},
pages = {9297},
abstract = {This article provides a comprehensive analysis of the feature extraction methods applied to vibro-acoustic signals (VA signals) in the context of robot-assisted interventions. The primary objective is to extract valuable information from these signals to understand tissue behaviour better and build upon prior research. This study is divided into three key stages: feature extraction using the Cepstrum Transform (CT), Mel-Frequency Cepstral Coefficients (MFCCs), and Fast Chirplet Transform (FCT); dimensionality reduction employing techniques such as Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP); and, finally, classification using a nearest neighbours classifier. The results demonstrate that using feature extraction techniques, especially the combination of CT and MFCC with dimensionality reduction algorithms, yields highly efficient outcomes. The classification metrics (Accuracy, Recall, and F1-score) approach 99%, and the clustering metric is 0.61. The performance of the CT–UMAP combination stands out in the evaluation metrics.},
note = {Publisher: Multidisciplinary Digital Publishing Institute},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This article provides a comprehensive analysis of the feature extraction methods applied to vibro-acoustic signals (VA signals) in the context of robot-assisted interventions. The primary objective is to extract valuable information from these signals to understand tissue behaviour better and build upon prior research. This study is divided into three key stages: feature extraction using the Cepstrum Transform (CT), Mel-Frequency Cepstral Coefficients (MFCCs), and Fast Chirplet Transform (FCT); dimensionality reduction employing techniques such as Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP); and, finally, classification using a nearest neighbours classifier. The results demonstrate that using feature extraction techniques, especially the combination of CT and MFCC with dimensionality reduction algorithms, yields highly efficient outcomes. The classification metrics (Accuracy, Recall, and F1-score) approach 99%, and the clustering metric is 0.61. The performance of the CT–UMAP combination stands out in the evaluation metrics.
Sühn, T; Esmaeili, N; Mattepu, S; Spiller, M; Boese, A; Urrutia, R; Poblete, V; Hansen, C; Lohmann, C; Illanes, A; Friebe, M
Vibro-Acoustic Sensing of Instrument Interactions as a Potential Source of Texture-Related Information in Robotic Palpation Journal Article
In: Sensors, vol. 23, no. 6, pp. 3141, 2023, ISSN: 1424-8220, (Publisher: Multidisciplinary Digital Publishing Institute).
@article{suhn_vibro-acoustic_2023,
title = {Vibro-Acoustic Sensing of Instrument Interactions as a Potential Source of Texture-Related Information in Robotic Palpation},
author = {T Sühn and N Esmaeili and S Mattepu and M Spiller and A Boese and R Urrutia and V Poblete and C Hansen and C Lohmann and A Illanes and M Friebe},
url = {https://www.mdpi.com/1424-8220/23/6/3141},
doi = {10.3390/s23063141},
issn = {1424-8220},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Sensors},
volume = {23},
number = {6},
pages = {3141},
abstract = {The direct tactile assessment of surface textures during palpation is an essential component of open surgery that is impeded in minimally invasive and robot-assisted surgery. When indirectly palpating with a surgical instrument, the structural vibrations from this interaction contain tactile information that can be extracted and analysed. This study investigates the influence of the parameters contact angle α and velocity v→ on the vibro-acoustic signals from this indirect palpation. A 7-DOF robotic arm, a standard surgical instrument, and a vibration measurement system were used to palpate three different materials with varying α and v→. The signals were processed based on continuous wavelet transformation. They showed material-specific signatures in the time–frequency domain that retained their general characteristic for varying α and v→. Energy-related and statistical features were extracted, and supervised classification was performed, where the testing data comprised only signals acquired with different palpation parameters than for training data. The classifiers support vector machine and k-nearest neighbours provided 99.67% and 96.00% accuracy for the differentiation of the materials. The results indicate the robustness of the features against variations in the palpation parameters. This is a prerequisite for an application in minimally invasive surgery but needs to be confirmed in realistic experiments with biological tissues.},
note = {Publisher: Multidisciplinary Digital Publishing Institute},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The direct tactile assessment of surface textures during palpation is an essential component of open surgery that is impeded in minimally invasive and robot-assisted surgery. When indirectly palpating with a surgical instrument, the structural vibrations from this interaction contain tactile information that can be extracted and analysed. This study investigates the influence of the parameters contact angle α and velocity v→ on the vibro-acoustic signals from this indirect palpation. A 7-DOF robotic arm, a standard surgical instrument, and a vibration measurement system were used to palpate three different materials with varying α and v→. The signals were processed based on continuous wavelet transformation. They showed material-specific signatures in the time–frequency domain that retained their general characteristic for varying α and v→. Energy-related and statistical features were extracted, and supervised classification was performed, where the testing data comprised only signals acquired with different palpation parameters than for training data. The classifiers support vector machine and k-nearest neighbours provided 99.67% and 96.00% accuracy for the differentiation of the materials. The results indicate the robustness of the features against variations in the palpation parameters. This is a prerequisite for an application in minimally invasive surgery but needs to be confirmed in realistic experiments with biological tissues.
2021
Sabieleish, M; Heryan, K; Boese, A; Hansen, C; Friebe, M; Illanes, A
Study of needle punctures into soft tissue through audio and force sensing: can audio be a simple alternative for needle guidance? Journal Article
In: International Journal of Computer Assisted Radiology and Surgery, vol. 16, no. 10, pp. 1683–1697, 2021, ISSN: 1861-6410, 1861-6429.
@article{sabieleish_study_2021,
title = {Study of needle punctures into soft tissue through audio and force sensing: can audio be a simple alternative for needle guidance?},
author = {M Sabieleish and K Heryan and A Boese and C Hansen and M Friebe and A Illanes},
url = {https://link.springer.com/10.1007/s11548-021-02479-x},
doi = {10.1007/s11548-021-02479-x},
issn = {1861-6410, 1861-6429},
year = {2021},
date = {2021-10-01},
urldate = {2021-10-01},
journal = {International Journal of Computer Assisted Radiology and Surgery},
volume = {16},
number = {10},
pages = {1683–1697},
abstract = {Purpose Percutaneous needle insertion is one of the most common minimally invasive procedures. The clinician’s experience and medical imaging support are essential to the procedure’s safety. However, imaging comes with inaccuracies due to artifacts, and therefore sensor-based solutions were proposed to improve accuracy. However, sensors are usually embedded in the needle tip, leading to design limitations. A novel concept was proposed for capturing tip–tissue interaction information through audio sensing, showing promising results for needle guidance. This work demonstrates that this audio approach can provide important puncture information by comparing audio and force signal dynamics during insertion.
Methods An experimental setup for inserting a needle into soft tissue was prepared. Audio and force signals were synchronously recorded at four different insertion velocities, and a dataset of 200 recordings was acquired. Indicators related to different aspects of the force and audio were compared through signal-to-signal and event-to-event correlation analysis.
Results High signal-to-signal correlations between force and audio indicators regardless of the insertion velocity were obtained. The force curvature indicator obtained the best correlation performances to audio with more than 70% of the correlations higher than 0.6. The event-to-event correlation analysis shows that a puncture event in the force is generally identifiable in audio and that their intensities firmly related.
Conclusions Audio contains valuable information for monitoring needle tip/tissue interaction. Significant dynamics obtained from a well-known sensor as force can also be extracted from audio, regardless of insertion velocities.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Purpose Percutaneous needle insertion is one of the most common minimally invasive procedures. The clinician’s experience and medical imaging support are essential to the procedure’s safety. However, imaging comes with inaccuracies due to artifacts, and therefore sensor-based solutions were proposed to improve accuracy. However, sensors are usually embedded in the needle tip, leading to design limitations. A novel concept was proposed for capturing tip–tissue interaction information through audio sensing, showing promising results for needle guidance. This work demonstrates that this audio approach can provide important puncture information by comparing audio and force signal dynamics during insertion.
Methods An experimental setup for inserting a needle into soft tissue was prepared. Audio and force signals were synchronously recorded at four different insertion velocities, and a dataset of 200 recordings was acquired. Indicators related to different aspects of the force and audio were compared through signal-to-signal and event-to-event correlation analysis.
Results High signal-to-signal correlations between force and audio indicators regardless of the insertion velocity were obtained. The force curvature indicator obtained the best correlation performances to audio with more than 70% of the correlations higher than 0.6. The event-to-event correlation analysis shows that a puncture event in the force is generally identifiable in audio and that their intensities firmly related.
Conclusions Audio contains valuable information for monitoring needle tip/tissue interaction. Significant dynamics obtained from a well-known sensor as force can also be extracted from audio, regardless of insertion velocities.
Methods An experimental setup for inserting a needle into soft tissue was prepared. Audio and force signals were synchronously recorded at four different insertion velocities, and a dataset of 200 recordings was acquired. Indicators related to different aspects of the force and audio were compared through signal-to-signal and event-to-event correlation analysis.
Results High signal-to-signal correlations between force and audio indicators regardless of the insertion velocity were obtained. The force curvature indicator obtained the best correlation performances to audio with more than 70% of the correlations higher than 0.6. The event-to-event correlation analysis shows that a puncture event in the force is generally identifiable in audio and that their intensities firmly related.
Conclusions Audio contains valuable information for monitoring needle tip/tissue interaction. Significant dynamics obtained from a well-known sensor as force can also be extracted from audio, regardless of insertion velocities.
2020
Illanes, A; Boese, A; Friebe, M; Hansen, C
Feasibility Check: Can Audio Be a Simple Alternative to Force-Based Feedback for Needle Guidance? Book Section
In: Martel, A; Abolmaesumi, P; Stoyanov, D; Mateus, D; Zuluaga, M; Zhou, S; Racoceanu, D; Joskowicz, L (Ed.): Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, vol. 12263, pp. 24–33, Springer International Publishing, Cham, 2020, ISBN: 978-3-030-59715-3 978-3-030-59716-0, (Series Title: Lecture Notes in Computer Science).
@incollection{martel_feasibility_2020,
title = {Feasibility Check: Can Audio Be a Simple Alternative to Force-Based Feedback for Needle Guidance?},
author = {A Illanes and A Boese and M Friebe and C Hansen},
editor = {A Martel and P Abolmaesumi and D Stoyanov and D Mateus and M Zuluaga and S Zhou and D Racoceanu and L Joskowicz},
url = {https://link.springer.com/10.1007/978-3-030-59716-0_3},
doi = {10.1007/978-3-030-59716-0_3},
isbn = {978-3-030-59715-3 978-3-030-59716-0},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
booktitle = {Medical Image Computing and Computer Assisted Intervention – MICCAI 2020},
volume = {12263},
pages = {24–33},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {Accurate needle placement is highly relevant for puncture of anatomical structures. The clinician’s experience and medical imaging are essential to complete these procedures safely. However, imaging may come with inaccuracies due to image artifacts. Sensor-based solutions have been proposed for acquiring additional guidance information. These sensors typically require to be embedded in the instrument tip, leading to direct tissue contact, sterilization issues, and added device complexity, risk, and cost. Recently, an audio-based technique has been proposed for ”listening” to needle tip-tissue interactions by an externally placed sensor. This technique has shown promising results for different applications. But the relation between the interaction event and the generated audio excitation is still not fully understood. This work aims to study this relationship, using a force sensor as a reference, by relating events and dynamical characteristics occurring in the audio signal with those occurring in the force signal. We want to show that dynamical information that a well-known sensor as force can provide could also be extracted from a low-cost and simple sensor such as audio. In this aim, the Pearson coefficient was used for signal-to-signal correlation between extracted audio and force indicators. Also, an event-to-event correlation between audio and force was performed by computing features from the indicators. Results show high values of correlation between audio and force indicators in the range of 0.53 to 0.72. These promising results demonstrate the usability of audio sensing for tissue-tool interaction and its potential to improve telemanipulated and robotic surgery in the future.},
note = {Series Title: Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Accurate needle placement is highly relevant for puncture of anatomical structures. The clinician’s experience and medical imaging are essential to complete these procedures safely. However, imaging may come with inaccuracies due to image artifacts. Sensor-based solutions have been proposed for acquiring additional guidance information. These sensors typically require to be embedded in the instrument tip, leading to direct tissue contact, sterilization issues, and added device complexity, risk, and cost. Recently, an audio-based technique has been proposed for ”listening” to needle tip-tissue interactions by an externally placed sensor. This technique has shown promising results for different applications. But the relation between the interaction event and the generated audio excitation is still not fully understood. This work aims to study this relationship, using a force sensor as a reference, by relating events and dynamical characteristics occurring in the audio signal with those occurring in the force signal. We want to show that dynamical information that a well-known sensor as force can provide could also be extracted from a low-cost and simple sensor such as audio. In this aim, the Pearson coefficient was used for signal-to-signal correlation between extracted audio and force indicators. Also, an event-to-event correlation between audio and force was performed by computing features from the indicators. Results show high values of correlation between audio and force indicators in the range of 0.53 to 0.72. These promising results demonstrate the usability of audio sensing for tissue-tool interaction and its potential to improve telemanipulated and robotic surgery in the future.
2017
Poudel, P; Illanes, A; Arens, C; Hansen, C; Friebe, M
Active contours extension and similarity indicators for improved 3D segmentation of thyroid ultrasound images Proceedings Article
In: Cook, T; Zhang, J (Ed.): pp. 1013803, Orlando, Florida, United States, 2017.
@inproceedings{cook_active_2017,
title = {Active contours extension and similarity indicators for improved 3D segmentation of thyroid ultrasound images},
author = {P Poudel and A Illanes and C Arens and C Hansen and M Friebe},
editor = {T Cook and J Zhang},
url = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2254029},
doi = {10.1117/12.2254029},
year = {2017},
date = {2017-03-01},
urldate = {2025-08-14},
pages = {1013803},
address = {Orlando, Florida, United States},
abstract = {Thyroid segmentation in tracked 2D ultrasound (US) using active contours has a low segmentation accuracy mainly due to the fact that smaller structures cannot be efficiently recognized and segmented. To address this issue, we propose a new similarity indicator with the main objective to provide information to the active contour algorithm concerning the regions that the active contour should continue to expand or should stop. First, a preprocessing step is carried out in order to attenuate the noise present in the US image and to increase its contrast, using histogram equalization and a median filter. In the second step, active contours are used to segment the thyroid in each 2D image of the dataset. After performing a first segmentation, two similarity indicators (ratio of mean square error, MSE and correlation between histograms) are computed at each contour point of the initial segmented thyroid between rectangles located inside and outside the obtained contour. A threshold is used on a final indicator computed from the other two indicators to find the probable regions for further segmentation using active contours. This process is repeated until no new segmentation region is identified. Finally, all the segmented thyroid images passed through a 3D reconstruction algorithm to obtain a 3D volume segmented thyroid. The results showed that including similarity indicators based on histogram equalization and MSE between inside and outside regions of the contour can help to segment difficult areas that active contours have problem to segment.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Thyroid segmentation in tracked 2D ultrasound (US) using active contours has a low segmentation accuracy mainly due to the fact that smaller structures cannot be efficiently recognized and segmented. To address this issue, we propose a new similarity indicator with the main objective to provide information to the active contour algorithm concerning the regions that the active contour should continue to expand or should stop. First, a preprocessing step is carried out in order to attenuate the noise present in the US image and to increase its contrast, using histogram equalization and a median filter. In the second step, active contours are used to segment the thyroid in each 2D image of the dataset. After performing a first segmentation, two similarity indicators (ratio of mean square error, MSE and correlation between histograms) are computed at each contour point of the initial segmented thyroid between rectangles located inside and outside the obtained contour. A threshold is used on a final indicator computed from the other two indicators to find the probable regions for further segmentation using active contours. This process is repeated until no new segmentation region is identified. Finally, all the segmented thyroid images passed through a 3D reconstruction algorithm to obtain a 3D volume segmented thyroid. The results showed that including similarity indicators based on histogram equalization and MSE between inside and outside regions of the contour can help to segment difficult areas that active contours have problem to segment.