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A new method for sperm characterization for infertility treatment: hypothesis testing by using combination of watershed segmentation and graph theory.

Shojaedini SV, Heydari M - J Med Signals Sens (2014)

Bottom Line: Then decision about each hypothesis is done in following steps: Selecting some primary regions as candidates for sperms by watershed-based segmentation, pruning of some false candidates during successive frames using graph theory concept and finally confirming correct sperms by using their movement trajectories.The obtained results show the proposed method may detect 97% of sperms in presence of 5% false detections and track 91% of moving sperms.Furthermore, it can be shown that better characterization of sperms in proposed algorithm doesn't lead to extracting more false sperms compared to some present approaches.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, Iranian Research Organization for Science and Technology, Tehran, Iran.

ABSTRACT
Shape and movement features of sperms are important parameters for infertility study and treatment. In this article, a new method is introduced for characterization sperms in microscopic videos. In this method, first a hypothesis framework is defined to distinguish sperms from other particles in captured video. Then decision about each hypothesis is done in following steps: Selecting some primary regions as candidates for sperms by watershed-based segmentation, pruning of some false candidates during successive frames using graph theory concept and finally confirming correct sperms by using their movement trajectories. Performance of the proposed method is evaluated on real captured images belongs to semen with high density of sperms. The obtained results show the proposed method may detect 97% of sperms in presence of 5% false detections and track 91% of moving sperms. Furthermore, it can be shown that better characterization of sperms in proposed algorithm doesn't lead to extracting more false sperms compared to some present approaches.

No MeSH data available.


Extracted sperms using proposed algorithm in frames (a) 15, (b) 30, (c) 45 and (d) 60
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Figure 3: Extracted sperms using proposed algorithm in frames (a) 15, (b) 30, (c) 45 and (d) 60

Mentions: The proposed method was implemented using Matlab 2009. Additionally, three other recent algorithms were selected to implement and compare with the proposed algorithm. These alternative algorithms were: (1) Mean shift algorithm (MSA) which has been introduced in[9] and is called (MSA) for brevity in this article, (2) split and merge segmentation followed by nearest neighborhood which has been introduced in[8] and is called (SMNN) for brevity in this article and OF Algorithm which has been introduced in[14] and is called (OF) for brevity in this article. For brevity some results of the proposed and OF methods have been graphically showed in this part of article, but the complete statistics of the test results will be discussed in part IV. The captured videos were first processed using manual detection and tracking to obtain ground-truth tracks to compare the automatic methods with. Then tracked sperms were obtained by applying the proposed and other three alternative algorithms, and then the performance of each algorithm was determined by comparing of its results with manual results. Figures 2 and 3 show results which have been obtained in four different frames (15, 30, 45 and 60) by utilizing the OF and proposed methods, respectively. For example Figure 2a shows totally 63 sperms including 5 constant and 58 moving sperms in frame 15 of a test video. In this figure the OF method has extracted 56 sperms without any false detection. Figure 2b-d show 46 complete and 11 incomplete trajectories have been extracted from totally 58 moving sperms by using this algorithm. Furthermore, one trajectory has been missed. Figure 3 shows the obtained results of applying the proposed algorithms on the frames which had been shown in Figure 2. In frame 15 [Figure 3a] it is obvious that the proposed method has extracted 56 particles without false alarms. The results of frames 30, 45 and 60 (i.e. Figures 3b to c and d) show that this algorithm has extracted 53 full and 5 incomplete trajectories which shows that applying the proposed method on the same video has led to better results than OF.


A new method for sperm characterization for infertility treatment: hypothesis testing by using combination of watershed segmentation and graph theory.

Shojaedini SV, Heydari M - J Med Signals Sens (2014)

Extracted sperms using proposed algorithm in frames (a) 15, (b) 30, (c) 45 and (d) 60
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4236806&req=5

Figure 3: Extracted sperms using proposed algorithm in frames (a) 15, (b) 30, (c) 45 and (d) 60
Mentions: The proposed method was implemented using Matlab 2009. Additionally, three other recent algorithms were selected to implement and compare with the proposed algorithm. These alternative algorithms were: (1) Mean shift algorithm (MSA) which has been introduced in[9] and is called (MSA) for brevity in this article, (2) split and merge segmentation followed by nearest neighborhood which has been introduced in[8] and is called (SMNN) for brevity in this article and OF Algorithm which has been introduced in[14] and is called (OF) for brevity in this article. For brevity some results of the proposed and OF methods have been graphically showed in this part of article, but the complete statistics of the test results will be discussed in part IV. The captured videos were first processed using manual detection and tracking to obtain ground-truth tracks to compare the automatic methods with. Then tracked sperms were obtained by applying the proposed and other three alternative algorithms, and then the performance of each algorithm was determined by comparing of its results with manual results. Figures 2 and 3 show results which have been obtained in four different frames (15, 30, 45 and 60) by utilizing the OF and proposed methods, respectively. For example Figure 2a shows totally 63 sperms including 5 constant and 58 moving sperms in frame 15 of a test video. In this figure the OF method has extracted 56 sperms without any false detection. Figure 2b-d show 46 complete and 11 incomplete trajectories have been extracted from totally 58 moving sperms by using this algorithm. Furthermore, one trajectory has been missed. Figure 3 shows the obtained results of applying the proposed algorithms on the frames which had been shown in Figure 2. In frame 15 [Figure 3a] it is obvious that the proposed method has extracted 56 particles without false alarms. The results of frames 30, 45 and 60 (i.e. Figures 3b to c and d) show that this algorithm has extracted 53 full and 5 incomplete trajectories which shows that applying the proposed method on the same video has led to better results than OF.

Bottom Line: Then decision about each hypothesis is done in following steps: Selecting some primary regions as candidates for sperms by watershed-based segmentation, pruning of some false candidates during successive frames using graph theory concept and finally confirming correct sperms by using their movement trajectories.The obtained results show the proposed method may detect 97% of sperms in presence of 5% false detections and track 91% of moving sperms.Furthermore, it can be shown that better characterization of sperms in proposed algorithm doesn't lead to extracting more false sperms compared to some present approaches.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, Iranian Research Organization for Science and Technology, Tehran, Iran.

ABSTRACT
Shape and movement features of sperms are important parameters for infertility study and treatment. In this article, a new method is introduced for characterization sperms in microscopic videos. In this method, first a hypothesis framework is defined to distinguish sperms from other particles in captured video. Then decision about each hypothesis is done in following steps: Selecting some primary regions as candidates for sperms by watershed-based segmentation, pruning of some false candidates during successive frames using graph theory concept and finally confirming correct sperms by using their movement trajectories. Performance of the proposed method is evaluated on real captured images belongs to semen with high density of sperms. The obtained results show the proposed method may detect 97% of sperms in presence of 5% false detections and track 91% of moving sperms. Furthermore, it can be shown that better characterization of sperms in proposed algorithm doesn't lead to extracting more false sperms compared to some present approaches.

No MeSH data available.