# Measurement of Fish Morphological Features through Image Processing and Deep Learning Techniques
###### Abstract
Convolutional neural networks (CNN) were used, but no further implementation details were presented in [10]. Fish recognition from underwater images poses several challenges, such as poor image quality, unexpected objects, distortion and refraction. A machine learning approach was described in [11] to identify the fish species by extracting features through the speedup robust features (SURF) method. Then, a support vector machine (SVM) was used for fish classification by analyzing underwater images. The estimation of the fish's freshness is another domain where image processing and machine learning techniques can be applied. Fish disease can spread overnight through water to neighboring aquafarms. Therefore, high-speed and precise diagnosis is required. For example, in [12], a study that showed the sensitivity of fish eye tissue to pesticide was presented. A nondestructive method for measuring the sensitivity of fish exposed to pesticides was presented in [13]. The fish eye tissue was used for the extraction of spatial and statistical features that were affected by pesticides. Different classifiers were used, and the accuracy of the identification was 96.87%. Fish freshness was also examined in [14], where a fish sample was segmented and its features were tactically extracted using wavelet transformation and a Haar filter. Decomposition was performed in three levels to assess the freshness. The k-nearest neighbors (k-NN) classifier was used with a correct detection rate of 90%. Several image processing techniques have been developed to study fish behavior, swimming speed and size under a wide range of culture conditions. In [15], video was analyzed to measure the average fish size in a nonintrusive fashion based on stereo geometry. In a similar approach based on stereo vision [16], the _Oplegathus punctatus_ fish's body length was measured using LabVIEW. The fish's snout tip and caudal peduncle were extracted via contour and convex hull calculation. A review of fish biomass estimation methods was presented in [17]. The machine vision, acoustics and resistivity counter methods were reviewed, among others. Several recent approaches exploit machine learning techniques to align landmarks on the fish body. In [18], 23 landmarks were placed on 13,686 zooids. Regions of interest (ROIs) were extracted from each image, containing or not containing the object of interest within a bounding box. Histogram of oriented gradient (HOG) features of these regions were used to define a training set used by an SVM classifier. During testing, the classifier was scanned over an image pyramid using a sliding window. If the feature values in a window exceeded a threshold, they were considered to contain an object after performing non-maximum suppression [19]. Morphological feature indicators based on a mask region-CNN were extracted in [20]. Focus was given to the oval squid only, and the camera was placed at a known distance from the object. A trained ResNext101 model was used [21] as well as an FPN [22] network to obtain the corresponding feature map. Then, a fixed ROI was preset for each point in the feature map, and multiple ROIs were used in a binary classification and bounding box regression [23]. The aforementioned classification was used to separate the background. The application presented in this paper determines (1) the contour, (2) landmark positions and (3) location of specific fish parts from a photograph that displays one or more fish. The photograph can be captured underwater or ashore. If more than one fish is displayed, the results concern the one that is recognized with higher confidence. The contour and landmark positions are used to estimate the fish dimensions (length, height and area). The mass of the fish can be easily estimated from these dimensions through scaling. The combination of the landmark position and the contour can also determine the regions where the following parts reside: the caudal fin, spiny and soft dorsal, pelvic and anal fins and fish mouth.