Anemia is a blood abnormality that affects the quantity and quality of red blood cells in the human body. This sometimes banal sign spares no continent and no social stratum. This anomaly is generally appreciated through biological analyzes of patients ’ blood. These analyzes, which boil down to the knowledge of hemato-metric constants, cannot by themselves allow the characterization of certain forms of anemia in the sense that most anemia are related to the morphology and color of red blood cells. Our work in this paper is to perform blood smears on patients and perform a morphological and colorimetric analysis of red blood cells on these smears. This approach allowed us to highlight on each erythrocyte morphological and colorimetric descriptors to accurately identify the types of anemia by image processing methods. This identification is performed in an automated environment to allow pathologists to respond quickly to anemia-related emergencies and also improve the treatment to be conducted. This automation required the implementation of a new approach to electronic instrumentation and the acquisition of microscopic blood smear images for the automatic and rapid diagnosis of anemia.
With 1.62 billion people diagnosed worldwide, according to the database of the World Health Organization (WHO), anemia is a major public health problem [
Their characterization becomes paramount in that it can influence decision-making via diagnosis.
Automata already exist to perform the blood count which is an automated examination to assess certain types of anemia. Indeed, they make it possible to obtain information on the number and size of red blood cells [
In addition, medical personnel sometimes use manual methods to characterize anemia. This detection technique is rudimentary, difficult and very subjective (observation of the coloring of palms, conjunctivae and nails ...). For effective management of patients and to overcome the shortcomings of the various methods mentioned above, we propose a new instrumentation approach for the automatic and rapid diagnosis of anemia.
The work we are conducting reveals morphological and colorimetric descriptors that are discriminant extracted on each form of red blood cells. In this perspective, work has been done in the literature. Indeed, according to Chantal Fossat et al. in 2006 [
The implementation of an automated system according to Das et al. in 2012 [
The characterization of the morphological and colorimetric of the red blood cells requires the production of blood smears on the various samples taken from healthy patients or from anemic patients. This realization requires the equipment of
The laboratory data come from the samples of thirty (30) healthy people and one hundred and twenty (120) anemic people. We carried out five (05) blood smears per patient and, after the visual analysis of the practitioners we chose two (02) blood smears respecting the recommendations of the World Health Organization (WHO) [
The blood smear consists of spreading a drop of blood evenly on an object slide, so as to obtain a single layer on half of the surface of the slide [
• Mark the slide.
• Apply a drop of venous blood 1 cm from the tip of the object’s blade, placed on a hard, horizontal plane.
• Hold the first slide with one hand and tilt the second slide 45° just in front of the drop.
• Gradually put the slide in contact with the drop of blood.
• Let the blood flow along the stop of the second blade and before it reaches the edges, in a fast, uniform and continuous movement, pull the blood forward.
• Dry quickly while shaking (
There are two staining strategies with Giemsa: the fast method (10% dye) and the slow method (3% dye). The first is used in busy clinics and laboratories where speed of diagnosis is an essential element in the management of patients. The slow method is used to stain more slides, as is the case in epidemiological surveys. Given the large influx in the laboratories and the expected result we preferred the staining method to 10%.
There are usually two techniques for staining blood smears (by overlay and bath). We chose the bath staining technique instead of the stain technique. This technique has the advantage of staining several blood smears at a time, which is what guided our choice.
The process of bath staining of blood smears consists in following in chronological order the following steps:
• Preparation of three jars, the first containing a pure solution of May-Grunewald. the second containing a buffer solution and finally the third jar a solution of Giemsa diluted 1/10.
• Dive into the first jar of blood smears for five minutes (5 min).
• Transfer of blood smears into the jar containing the buffer solution.
• Removal of the smears from the buffer solution and soaking for 15 minutes in the jar containing Giemsa diluted 1/10 with distilled water pH 7.2.
• Rinse thoroughly 2 - 3 times each slide under running water.
• Exposure of the blades to the open air for drying.
• Wait for complete drying before the acquisition (
The microscope and the Moticam 2.0 camera are mounted to make it a single entity. The block thus obtained is connected with a USB cable to the microcomputer on which the Moticam 3.0 software is installed, see
1) The Characteristics of the Equipment
Capturing microscopic images for the proper conduct of our work required the acquisition of the following equipment: a microscope, a camera and a laptop. The characteristics of the camera and the microscope are grouped respectively in
1) The Characteristics of the Computer
The entire device runs on an hp laptop with the following features:
• Processor: Intel® CoreTM i3-5005U processor at 2.00 GHz at 2.00 GHz.
• Operating system: Windows10 Professional.
• RAM: 4, 00 Go.
• System type: 64-bit operating system, ×64 processors.
The proposed method starts from the acquisition of microscopic color images of the selected blood smears. Then we treat and characterize the different morphological and colorimetric parameters of each form of red blood cell (
The morphological and colorimetric characterization of red blood cells requires the extraction of different discriminating descriptors for each form of red blood cells. Indeed, the recognition of red blood cell forms is based on measurable data extracted on red blood cells. These data or characteristics must be discriminating in order to achieve good identification [
In this approach we need to isolate the discriminating red blood cell by an image processing tool that has segmentation.
1) Segmentation
Segmentation is the partition of an image into several regions according to a well defined criterion and having pixels of the same characteristics. The main purpose of this treatment is to extract the information that must allow a precise identification of the object concerned [
Parameters values | Values |
---|---|
Moticam | 2 |
Sensor type | CMOS |
Optical | 1/3'' |
Actives pixels | 1600 × 1200 |
Pixel size | 3.2 µm × 3.2 µm |
Imaging Area | 5.12 mm × 3.84 mm |
Scan Mode | progressive |
Shutter type | Rolling with Global reset |
Operating temp | −10˚C to 60˚C non condensing |
Max. SNR | 43 dB |
D/R | 61 dB |
Interface | USB2.0 |
Operating req. | Windows7 or higher, osx, Linux, 250 MB free HD space |
Parameters | Values |
---|---|
Mechanism Tube length | 160 mm |
Viewing Head | Compensation Free Trinocular Head, inclined at 30, interpupillar distance 55 - 75 mm |
Eyepiece | Viewfield line 18 mm |
Nosepiece | Forward Quadruple Nosepiece |
Object | Achromatique: 4×, 10×, 40×, 100× |
Focus System | Coaxial coarse and Fine Focusing System, sensitivity and Graduation of Fine Focus: 0.002 mm. coarse & fine focus range: 23 mm |
Condenser | Abel, NA = 1.25 |
Stage | Double layer mechanical stage, area: 140 × 140mm, movement range: 75 × 50 mm |
Lamp-House | Halogenlamp 6 V 20 W |
the study by P. Shivhare et al., listed three main methods of segmentation [
• Edge-based segmentation
• Segmentation based on the region
• Segmentation based on thresholding
a) Edge-based segmentation
This method is based on the abrupt change in pixel luminance that determines the contour. It makes it possible to mark a border or a real transition zone between the regions (the objects) and the background of the image.
b) Segmentation based on the region
The region in an image is the grouping of a number of pixels having similar values. This method then makes it possible to group a number of homogeneous pixels or having common attributes [
c) Segmentation based on thresholding
Thresholding segments an image into two classes. The intensity of each pixel is compared to the defined threshold and that pixel by pixel. When this intensity is below the threshold, the pixel takes the value 0 and the value 1 otherwise. This thresholding operation applied to the entire image leads to binarization of the image [
2) Morphological descriptors
Healthy red blood cells have a uniformly rounded and undeformed shape whereas those that are anemic have various forms that are often specific to the type of anemia. This morphological deformation can be characterized by shape descriptors:
a) Area of the red blood cells
The surface of the red cell or area is the set of pixels covering the segmented image.
area = ∑ x ∑ y f ( x , y ) (1)
f(x, y) is the pixel whose position is represented by the pair of x and y coordinates in the binarized image. It is 1 when the pixel is in the segmented region and 0 otherwise.
b) Perimeter of the red blood cells (P)
The perimeter is the sum of the pixels on the edge of the segmented image. To calculate it we will use the eight-connexity method (
We go through the whole contour. When a pixel has a connectivity lower than 8 this pixel belongs to the outline.
P = ∑ x ∑ y f ( x , y ) (2)
c) Compactness (C)
The compactness of the region is the ratio between the area of this region and the perimeter. It measures the regularity of the surface of the region
C = 4 × π × area p 2 (3)
d) Eccentricity (e)
Eccentricity describes the degree of elongation of a red blood cell.
e = ( 1 − b 2 a 2 ) 1 / 2 (4)
The variable b represents the minor axis and the variable a represents the major axis. The value of eccentricity varies between 0 and 1 for red blood cell forms. When e = 0 the object is practically a circle, when e < 1 the object is lengthened.
e) Convex set
Let two distinct pixels x and y belong to the same red cell ( E ) . The number of pixels separating them describes a segment [x, y]. If the entire segment belongs to the cell whatever x and y then the set is convex. This method allows us to clearly characterize certain erythrocytes.
{ ( E ) convex if ∀ x , y ∈ ( E ) , t x + ( 1 − t ) y , t ∈ [ 0 , 1 ] } (5)
The segment [x, y] is defined as follows:
{ ( x , y ) / t x + ( 1 − t ) y , t ∈ [ 0 , 1 ] } (6)
t is the variable describes the segment [x, y]
3) Color Descriptors
a) Average pixel intensity
An image is a set of pixels, each of which has a value that defines its intensity. So the average intensity of this region can be known. We have therefore from the
(x − 1, y − 1) | (x, y − 1) | (x + 1, y − 1) |
---|---|---|
(x − 1, y) | (x, y) | (x + 1, y) |
(x − 1, y + 1) | (x, y + 1) | (x + 1, y + 1) |
algorithm below calculated the average intensity of the pixels for each region. The algorithm is designed under MATLAB 2016a. Its principle is defined hereafter:
b = view image (filename);
im = read image (filename);
im2 = converting colors to grayscale (im);
h = selection region;
bw = conversion to binary image (h, b);
view image (bw);
Imoy = Mean color of object in white.
b) Entropy
Entropy is a statistical measure to obtain information. It becomes an important parameter to characterize an image.
H ( x ) = ∑ i = 1 n P i ⋅ log 2 ( P i ) (7)
c) Standard deviation (σ)
This statistical tool is the gap between the intensity of each pixel to the average of the pixel intensity.
σ = 1 n − 1 ∑ i = 1 n ( x i − x ¯ ) 2 (8)
d) Percentage of colored area in red (PCol)
The colored part of the red blood cell contains hemoglobin hence the red color of the cells.
PCol = ( ( areag − areab ) / areag ) ∗ 100 (9)
Areag: represents the number of pixels of the total area of the red blood cell,
Areab: the number of pixels of the central white part of the erythrocyte,
PCol: represents the percentage of ocqupation of the colored area of the red blood cell.
e) Area of the white zone of the red blood cells
areablc = areag − areacol (10)
pblc = ( areablc / areag ) × 100 (11)
Areablc: is the set of pixels in the central area of the red cell,
Areacol: the set of pixels in the colored area.
Examination of the blood smear makes it possible to carry out a morphological and colorimetric study of the various figured elements of the blood. For our work, only red blood cells interest us. On a normal smear, red blood cells have rounded shapes, without nuclei and the same color. Any modification of these parameters reflects a pathological condition as shown in Figures 8-10 below. Anemia is a consequence of these changes.
In this section, we will present the results obtained during the acquisition and identification phase of red blood cells.
For each blood smear we made images of several optical fields of different patients. In order to obtain quality and exploitable images, we played on certain parameters of the camera. These are the following parameters (
• Color (Gain and brightness of RGB colors).
• Brightness.
• Resolution, exposure, contrast, gain.
• White balance, gamma.
We present in this section some images.
Anemia with hypochromic erythrocytes is anemia that is caused by iron deficiency in the body. It is also called iron deficiency anemia. This type of anemia is common in medicine. It is characterized by a pallor of red blood cells with the white central area more developed and it is found that the hemoglobin (red part) is deposited at the periphery of the red blood cells forming a ring as shown in
The characterization of a red blood cell requires both measured morphological and colorimetric data. The success of this action leads to the use of an image processing tool: segmentation. The advantage of this tool is to isolate the cell in order to extract information that can identify it accurately. Segmentation methods are numerous [
cap269 | area | perimeter | compactness | eccentricity | pbinary | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
H1 | 5559 | 274 | 0.93048 | 0.3400 | 0 | |||||||
5557 | 270 | 0.95791 | 0.2910 | 0 | ||||||||
5466 | 273 | 0.92162 | 0.3212 | 0 | ||||||||
MH1 | 5527.3333 | 272.3333 | 0.93667 | 0.3174 | 0 | |||||||
H2 | 5416 | 273 | 0.9132 | 0.2803 | 0 | |||||||
5400 | 267 | 0.9519 | 0.3287 | 0 | ||||||||
5598 | 272 | 0.9508 | 0.1793 | 0 | ||||||||
MH2 | 5471.3333 | 270.6667 | 0.9386 | 0.2628 | 0 | |||||||
H3 | 5727 | 278 | 0.9312 | 0.3823 | 0 | |||||||
5800 | 279 | 0.9363 | 0.3162 | 0 | ||||||||
5717 | 274 | 0.9569 | 0.2847 | 0 | ||||||||
MH3 | 5748 | 277 | 0.9415 | 0.3278 | 0 | |||||||
cap269 | intMoy | std | colMR | ColMG | ColMB | %colored | %white |
---|---|---|---|---|---|---|---|
H1 | 202 | 5.1568 | 255 | 224 | 220 | 94.2616 | 5.7384 |
202 | 5.1547 | 255 | 224 | 220 | 94.3134 | 5.6865 | |
202 | 5.1153 | 255 | 224 | 220 | 94.1090 | 5.8910 | |
MH1 | 202 | 5.1423 | 255 | 224 | 220 | 94.2280 | 5.7720 |
H2 | 204 | 5.7267 | 255 | 224 | 220 | 83.4564 | 16.5436 |
204 | 5.7797 | 255 | 224 | 220 | 84.0741 | 15.9259 | |
204 | 5.8043 | 255 | 224 | 220 | 84.8875 | 15.1125 | |
MH2 | 204 | 5.7702 | 255 | 224 | 220 | 84.1393 | 15.8606 |
H3 | 204 | 5.4969 | 255 | 224 | 220 | 90.3964 | 9.6036 |
204 | 5.5139 | 255 | 224 | 220 | 90.1724 | 9.8276 | |
204 | 5.4615 | 255 | 224 | 220 | 89.9948 | 10.0052 | |
MH3 | 204 | 5.4908 | 255 | 224 | 220 | 90.1878 | 9.8121 |
Cap311 | area | perimeter | compactness | eccentricity | pbinary |
---|---|---|---|---|---|
H1 | 4171 | 241 | 0.9024 | 0.4940 | 0 |
4123 | 234 | 0.9462 | 0.5723 | 0 | |
4036 | 235 | 0.9184 | 0.3815 | 0 | |
MH1 | 4110 | 236.6667 | 0.9223 | 0.4826 | 0 |
H2 | 3403 | 215 | 0.9251 | 0.4013 | 0 |
3558 | 220 | 0.9238 | 0.4806 | 0 | |
3428 | 213 | 0.9495 | 0.4523 | 0 | |
MH2 | 3463 | 216 | 0.9328 | 0.4447 | 0 |
H3 | 3975 | 229 | 0.9525 | 0.3500 | 0 |
4294 | 240 | 0.9368 | 0.2797 | 0 | |
4097 | 234 | 0.9402 | 0.3008 | 0 | |
MH3 | 4122 | 234.3333 | 0.9432 | 0.3102 | 0 |
Cap311 | intMoy | std | colMR | ColMG | ColMB | %colored | %white |
---|---|---|---|---|---|---|---|
H1 | 228 | 18.2366 | 254 | 223 | 212 | 60.1295 | 39.8705 |
228 | 18.4479 | 254 | 223 | 212 | 62.6728 | 37.3272 | |
228 | 18.4187 | 254 | 223 | 212 | 59.6878 | 40.3122 | |
MH1 | 228 | 18.3678 | 254 | 223 | 212 | 60.8300 | 39.1700 |
H2 | 219 | 25.7382 | 254 | 223 | 212 | 62.8857 | 37.1143 |
219 | 25.5280 | 254 | 223 | 212 | 62.4227 | 37.5773 | |
219 | 25.7352 | 254 | 223 | 212 | 62.3396 | 37.6604 | |
MH2 | 219 | 25.6671 | 254 | 223 | 212 | 62.5493 | 37.4507 |
H3 | 219 | 24.9620 | 254 | 223 | 212 | 59.4214 | 40.5786 |
219 | 24.4978 | 254 | 223 | 212 | 62.8319 | 37.1681 | |
219 | 24.7150 | 254 | 223 | 212 | 61.5572 | 38.4428 | |
MH3 | 219 | 24.7249 | 254 | 223 | 212 | 61.2702 | 38.7298 |
Cap227 | area | perimeter | compactness | eccentricity | Pbinary |
---|---|---|---|---|---|
H1 | 2855 | 278 | 0.4642 | 0.8672 | 1 |
2981 | 276 | 0.4918 | 0.8813 | 1 | |
2971 | 275 | 0.4937 | 0.8553 | 1 | |
MH1 | 2935.6667 | 276.3333 | 0.4832 | 0.8680 | 1 |
H2 | 2208 | 207 | 0.6475 | 0.7560 | 1 |
2098 | 201 | 0.6526 | 0.8140 | 1 | |
2183 | 207 | 0.6402 | 0.8329 | 1 | |
MH2 | 2163 | 205 | 0.6468 | 0.8010 | 1 |
H3 | 3295 | 271 | 0.5638 | 0.8413 | 1 |
3358 | 275 | 0.5580 | 0.8372 | 1 | |
3309 | 268 | 0.5790 | 0.8346 | 1 | |
MH3 | 3320.6667 | 271.333 | 0.5669 | 0.8377 | 1 |
Cap227 | intMoy | std | colMR | ColMG | ColMB | %coloré | %blanc |
---|---|---|---|---|---|---|---|
H1 | 175 | 16.8154 | 255 | 222 | 215 | 100 | ABS |
175 | 18.3393 | 255 | 222 | 215 | 100 | ABS | |
175 | 18.1241 | 255 | 222 | 215 | 100 | ABS | |
MH1 | 175 | 17.7596 | 255 | 222 | 215 | 100 | ABS |
H2 | 184 | 20.8689 | 253 | 214 | 204 | 100 | ABS |
184 | 20.0974 | 253 | 214 | 204 | 100 | ABS | |
184 | 20.5583 | 253 | 214 | 204 | 100 | ABS | |
MH2 | 184 | 20.5082 | 253 | 214 | 204 | 100 | ABS |
H3 | 197 | 13.2418 | 255 | 213 | 206 | 100 | ABS |
197 | 14.3507 | 255 | 213 | 206 | 100 | ABS | |
197 | 13.8554 | 255 | 213 | 206 | 100 | ABS | |
MH3 | 197 | 13.8159 | 255 | 213 | 206 | 100 | ABS |
Cap229 | area | perimeter | compactness | eccentricity | Pbinary |
---|---|---|---|---|---|
H1 | 4013 | 251 | 0.8004 | 0.8727 | 0 |
3923 | 245 | 0.8213 | 0.8738 | 0 | |
4029 | 251 | 0.8036 | 0.8781 | 0 | |
MH1 | 3988.33 | 249 | 0.8085 | 0.8749 | 0 |
H2 | 4500 | 252 | 0.8905 | 0.7476 | 0 |
4801 | 267 | 0.8463 | 0.7860 | 0 | |
4801 | 267 | 0.8463 | 0.7860 | 0 | |
MH2 | 4700.667 | 262 | 0.8610 | 0.7732 | 0 |
H3 | 4272 | 256 | 0.8191 | 0.8513 | 0 |
4210 | 259 | 0.7887 | 0.8739 | 0 | |
4211 | 253 | 0.8267 | 0.8522 | 0 | |
MH3 | 4231 | 256 | 0.8115 | 0.8591 | 0 |
Cap229 | intMoy | std | colMR | ColMG | ColMB | %colored | %white |
---|---|---|---|---|---|---|---|
H1 | 232 | 17.1237 | 255 | 233 | 228 | 68.9758 | 31.0242 |
232 | 17.1677 | 255 | 233 | 228 | 67.0150 | 32.985 | |
232 | 17.0458 | 255 | 233 | 228 | 68.3544 | 31.645 | |
MH1 | 232 | 17.1122 | 255 | 233 | 228 | 68.115 | 31.8849 |
H2 | 208 | 19.5177 | 255 | 233 | 228 | 82.2222 | 17.7778 |
209 | 20.9228 | 255 | 233 | 228 | 82.9410 | 17.0589 | |
209 | 20.9228 | 255 | 233 | 228 | 83.2118 | 16.7882 | |
MH2 | 208 | 20.4545 | 255 | 233 | 228 | 82.7917 | 17.2083 |
H3 | 218 | 15.5455 | 255 | 233 | 228 | 80.2434 | 19.7565 |
218 | 15.4646 | 255 | 233 | 228 | 80.9263 | 19.0736 | |
218 | 15.0689 | 255 | 233 | 228 | 81.0021 | 18.9978 | |
MH3 | 218 | 15.3597 | 255 | 233 | 228 | 80.7239 | 19.2760 |
The tables are divided into two groups for each segmented red blood cell. In a first table are morphological descriptors and in a second color descriptors. In each table the horizontal side contains the following descriptors: area: represents the number of pixels lying on the whole surface of the segmented cell (white part in
In sickle cell patients red blood cells are sickle-shaped cells or banana cells. Like all acquired images we have made morphological measurements grouped in
Elliptocytes are elongated red blood cells with rounded ends.
H1: first red blood cell, MH1: mean red blood cell measurements H1, intMoy: average pixel intensity, colMR, ColMG, ColMB, average staining of RGB components, STD: standard deviation, % COLOR and % WHITE: percentage of the colored part and the white part of the red blood cells.
In this paper, we propose a new instrumentation approach to automatically characterize different anemia. The work done in this document consisted mainly of elaborating patient eligibility criteria for our study. Then we collected our samples and prepared the blood smears. The acquisition phase took place in an environment described in this paper. For a better identification of each of the forms of red blood cells specific to anemia types, we propose efficient and specific descriptors for both the geometric shapes and the color appearance of red blood cells. Thus, for a formal identification of shapes, we associated the geometric descriptors (area, perimeter, compactness, eccentricity, the convex set) with the colorimetric descriptors (mean pixel intensity, standard deviation, average color for each RGB component, percentage color and white). This combination of parameters allows good identification. The extraction of these morphological and colorimetric characteristics of normal and abnormal red blood cells was possible thanks to our semi-supervised contour selection segmentation method and to the algorithms we developed and implemented under Matlab 2016a. Our future work will concern the further identification of certain forms of red blood cells and will end with the classification of anemias based on the morphology and color of red blood cells.
The authors declare no conflicts of interest regarding the publication of this paper.
Alico, J.N., Ouattara, S. and Clement, A. (2020) A New Electronic Instrumentation Approach for the Acquisition of Microscopic Blood Smear Images for the Automatic Diagnosis of Anemia. Advances in Bioscience and Biotechnology, 11, 237-255. https://doi.org/10.4236/abb.2020.116018